Genome Analysis Wiki - User contributions [en]

Sara Rashkin

2013-06-27T16:42:06Z

Srashkin:

Sara is a PhD student working with [[Goncalo Abecasis]] in developing methods for copy number variation detection and is currently working on identifying optimal strategies for identifying disease associated singletons. She earned a BA in statistics from Northwestern University, where she graduated with departmental honors, and an MS in biostatistics from the University of Michigan.

Sara Rashkin

2013-06-27T16:41:03Z

Srashkin: Created page with 'Sara is a PhD student working with Goncalo Abecasis in developing methods for copy number variation detection as well as identifying optimal strategies for identifying diseas…'

Sara is a PhD student working with [[Goncalo Abecasis]] in developing methods for copy number variation detection as well as identifying optimal strategies for identifying disease associated singletons. She earned a BA in statistics from Northwestern University, where she graduated with departmental honors, and an MS in biostatistics from the University of Michigan.

Abecasis Lab

2013-06-27T16:34:40Z

Srashkin: /* Graduate Students */

[[Image:Abecasis_group_photo_cropped.jpg|900px|center|Group Photo 2013]]



== Mission Statement ==

We are developing and applying computational and statistical tools to further understanding of complex human diseases, such as cardiovascular disease and diabetes.

== Leadership ==

[[Goncalo Abecasis]] is currently the Felix Moore Collegiate Professor of Biostatistics at the University of Michigan School of Public Health.

== Current Members ==

=== Research Fellows ===

* Goo Jun
* Christian Fuchsberger
* Alex Tsoi
* [[Dajiang Liu]]
* [[Lars Fritsche]]
* [[Scott Vrieze]]

=== International Visitors ===

* Andrea Maschio
* Giorgio Pistis
* Eleanora Porcu

=== Graduate Students ===

* Su Chu

* Sayantan Das

* Shuang Feng

* Dan Hovelson

* [[Alan Kwong]]

* Ben Lerch

* [[Sara Rashkin]]

* Sebanti Sengupta

* Vivian Wang

* Xiaowei Zhan

* Tingting Zhou

=== Staff ===

* Laura Baker

* Tom Blackwell

* [[Sean Caron]]

* [[Jennifer Bragg-Gresham]]

* Kevin Li

* [[Mary Kate Wing]]

== Alumni ==

=== Former Research Faculty ===

Hyun Min Kang (''graduated in 2011''), now Assistant Professor at the [http://www.sph.umich.edu/biostat/ University of Michigan School of Public Health, Department of Biostatistics].

=== Former Research Fellows ===

Weimin Chen (graduated 2007), now Assistant Professor at the [http://people.virginia.edu/~wc9c/ Department of Public Health Sciences & Center for Public Health Genomics, University of Virginia]

Bingshan Li (graudated 2011), now Assistant Professor at the [https://medschool.vanderbilt.edu/cqs/people/Bingshan/Li/cqs-faculty-members Center for Quantitative Sciences, Vanderbilt University]

Serena Sanna (graduated 2007), now an investigator at the [http://www.serenasanna.com/ Istituto di Neurogenetica e Neurofarmacologia in Sardinia, Italy]

Paul Scheet (graduated 2008), now Assistant Professor at [http://faculty.mdanderson.org/Paul_Scheet/Default.asp?SNID=221605974 Department of Epidemiology, University of Texas MD Anderson Cancer Center]

Carlo Sidore (graduated 2012), now an investigator at the [http://www.serenasanna.com/ Istituto di Neurogenetica e Neurofarmacologia in Sardinia, Italy]

William Stewart (graduated 2008), now Assistant Professor at [http://www.mathmed.org/#William_Stewart Battelle Center for Computational Medicine, Departments of Statistics and Pediatrics, National Children's Hospital and Ohio State University]

=== Former Doctoral Students ===

Wei Chen (graduated 2011), now Assistant Professor at the [http://www.chp.edu/CHP/Chen%2C+Wei%2C+PhD Department of Pediatrics, University of Pittsburgh Medical Center]

Jun Ding (graduate 2010), now Staff Scientist / Facility Head at the [http://www.grc.nia.nih.gov/branches/lg/lg.htm Laboratory of Genetics, National Institute on Aging (NIH)].

Yun Li (graduated 2009), now Assistant Professor at the [http://www.sph.unc.edu/?option=com_profiles&Itemid=6138&profileAction=ProfDetail&pid=708777879 Department of Biostatistics, University of North Carolina].

Youna Hu (graduated 2012), now a Research Fellow [http://cteg.berkeley.edu/members/hu.html working with Rasmus Nielsen at Berkeley]

Mingyao Li (graduated 2005), now Associate Professor at the [http://www.cceb.upenn.edu/faculty/index.php?id=159 Department of Biostatistics and Epidemiology, University of Pennsylvania]

Liming Liang (graduated 2009), now Assistant Professor at the [http://www.hsph.harvard.edu/faculty/liming-liang/ Departments of Biostatistics and Epidemiology, Harvard University]

Tasha Fingerlin (graduated 2003), now Associate Professor at the [http://www.ucdenver.edu/academics/colleges/PublicHealth/departments/Epidemiology/About/Faculty/Pages/FingerlinT.aspx Section of Epidemiology and Community Health, University of Colorado Health Sciences Center]

Andrew Skol (graduated 2006), now Assistant Professor at the [http://med-www02.bsd.uchicago.edu/339/FacultyPro/faculty_profile.aspx?empl_id=10164 Section of Genetic Medicine, University of Chicago]

Jin Zhen (graduated 2009), now working in the Pharmaceutical Industry.

=== Former Masters Students ===

Melinda Curran

Vesela Gateva

Xijing Han

Elizabeth Jewell

Yanming Li

Heather Munro

Theresa Scott (nee Daigneault)

Matthew Snyder

Yuan Wei

Abigail Woodroffe

Zaojun Ye

Matthew Zawitowski

Anita Yu Zhao

=== Visitors ===

Toshiko Tanakato

== Really Useful Stuff ==

* [[Abecasis Group Awards]]
* [https://calendars.office.microsoft.com/pubcalstorage/m3n2kr0z1470909/Goncalo_Abecasis_Calendar(1).ics Goncalo's Calendar]

Base Caller Summaries

2010-03-12T21:04:23Z

Srashkin: /* References */

==Standard Illumina Base Caller (Bustard)==

===Sequencing-by-Synthesis (SBS)===

*DNA sample obtained, containing many copies of same sequences and randomly fragmented
*Single-stranded DNA fragments attached to slide and amplified so there is a cluster of each fragment
*DNA polymerase and 4 terminal bases (with distinct fluorescent markers) added
*Clusters excited by lasers and photos taken in optimal wavelengths for 4 fluorophores
*Fluorophores and terminators removed and process repeated for L cycles

===Image Analysis===
*Corrects for imperfect repositioning of camera and aberrations of lens by aligning images to reference from original cycle
*Signal for each cluster characterized as time series data of fluorescence intensities and noise

===Base Calling===
*Converts fluorescence signals into actual sequence data with quality scores
*Takes intensities of four channels for every cluster in each cycle and determines concentration of each base
*Renormalizes concentrations by multiplying by ratio of average concentrations in first cycle and current cycle
*Uses Markov model to determine transition matrix modeling probability of phasing (no new base synthesized), prephasing (two new bases synthesized), and normal incorporation
*Uses transition matrix and observed concentrations of each base to determine concentrations in absence of phasing and reports these as base calls
**Assumes crosstalk matrix constant for a given sequencing run and that phasing affects all nucleotides in the same way

===General Noise Factors===
*Phasing
**Failures in nucleotide incorporation or block removal or incorporation of more than one nucleotide in a particular cycle
*Fading
**Decay in fluorescent signal intensity with each cycle
**Likely attributable to material loss during sequencing
*Crosstalk
**C channel illumination overlaps with A: a C label fluoresces in A channel (similarly G and T overlap)
**Likely caused by overlap in dye emission frequencies
*T Accumulation
**The fluorophores used for thymine are not always removed properly after each iteration
**Intensity of T signal increases across sequencing run

==Alta-Cyclic==
===Training Stage===
*Learns run-specific noise patterns according to model and finds optimized solution reducing affect of noise sources using a Support Vector Machine (SVM)
*Half of training set used for cross-validation

===Base Calling Stage===
*Reports all sequences from run with optimized parameters

===Differences from Standard Illumina Base Caller===
*Calling parameters optimized empirically and tested to enhance accuracy of each run
*Calculates phasing parameters based on parametric model
*Dynamically tracks changes in crosstalk, which disrupt signals in later cycles

==Probabilistic Base Calling==
*Produces an alternative probabilistic base calling method based on the fluorescence intensity quantifications that uses:
**Extended IUPAC alphabet to code ambiguous bases
**Information criterion to control length of trustable reads
*Reduced systematic bias by addressing:
**Crosstalk
**Dephasing
**Optical effect that tiles in center of image appear brighter corrected by fitting a 2D loess model to intensities and subtracting difference between fit and median intensities
*Measure level of uncertainty in base calling by entropy (uncertainty in determination of correct kth base)
*Does not consider fine-tuning image analysis

==BayesCall==
*Model-based approach to base calling
*Main goal is to model sequencing process by taking stochasticity into account and by explicitly modeling how errors may arise
*Obtain base calls by maximizing posterior distribution of sequences given observed data and assuming a uniform prior on sequences

==Swift==
Performs both image analysis and base calling

===Image Analysis===
*Background subtraction – minimal pixel value within a window around each pixel subtracted from central pixel’s value
*Image correlation – alignment of images to reference cycle
*Object identification and intensity extraction

===Base Calling===
*Corrects for crosstalk by performing linear regression on crosstalk plots and use slope to derive correction matrix, performed iteratively until slope is zero
*Phasing correction by ranking clusters by chastity (the ratio of the highest intensity to the sum of the top two intensities) - use top 400 clusters to estimate phasing and apply it as a correction
*After correction, base with maximum intensity chosen as called base

==Ibis==

===Method===
*Estimate sequencing chemistry model as a parameter directly from data using statistical learning
*Training set from Bustard output using raw cluster intensities
*Used a base caller with SVM classifiers for each cycle that have intensity values of the current cycle as well as the previous and following cycles (if they exist)
*Data set created by aligning raw reads with mismatches for a fraction of the tiles to a reference sequence
**Half of this set used as a training set and the other half as a test set used to check results of training
*Estimate parameters for calculating a quality score given class assignment and distances to the classification/decision boundary from SVM

===Comparison===
*Unlike AltaCyclic, includes base-specific phasing parameters so can correct raw intensities for T accumulation
*Does not call an 'N' character for poor quality bases
*Process unique as causes of sequencing error not modeled separately
**Consider causes together by using neighboring signals in statistical learning procedure

==References==

Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J. (2008) Alta-Cyclic: A self-optimizing base caller for next-generation sequencing. ''Nature Methods'' '''5''':679-682

Kao, W.-C., Stevens, K., Song, Y.S. (2009) BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing. ''Genome Research'' '''19''':1884-1895

Kircher, M., Stenzel, U., Kelso, J. (2009) Improved base calling for the Illumina Genome Analyzer using machine learning strategies. ''Genome Biol.'' '''10(8)''':Article R83

Rougemont, J., Amzallag, A., Iseli, C., Farinelli, L., Xenarios, I., Naef, F. (2008) Probabilistic base calling of Solexa sequencing data. ''BMC Bioinformatics'' '''9''':Article 431

Whiteford, N., Skelly, T., Curtis, C., Ritchie, M.E., Löhr, A., Zaranek, A.W., Abnizova, I., Brown, C. (2009) Swift: Primary data analysis for the Illumina Solexa sequencing platform. ''Bioinformatics'' '''25''':2194-2199

Base Caller Summaries

2010-03-12T20:57:06Z

Srashkin: /* Comparison */

==Standard Illumina Base Caller (Bustard)==

===Sequencing-by-Synthesis (SBS)===

*DNA sample obtained, containing many copies of same sequences and randomly fragmented
*Single-stranded DNA fragments attached to slide and amplified so there is a cluster of each fragment
*DNA polymerase and 4 terminal bases (with distinct fluorescent markers) added
*Clusters excited by lasers and photos taken in optimal wavelengths for 4 fluorophores
*Fluorophores and terminators removed and process repeated for L cycles

===Image Analysis===
*Corrects for imperfect repositioning of camera and aberrations of lens by aligning images to reference from original cycle
*Signal for each cluster characterized as time series data of fluorescence intensities and noise

===Base Calling===
*Converts fluorescence signals into actual sequence data with quality scores
*Takes intensities of four channels for every cluster in each cycle and determines concentration of each base
*Renormalizes concentrations by multiplying by ratio of average concentrations in first cycle and current cycle
*Uses Markov model to determine transition matrix modeling probability of phasing (no new base synthesized), prephasing (two new bases synthesized), and normal incorporation
*Uses transition matrix and observed concentrations of each base to determine concentrations in absence of phasing and reports these as base calls
**Assumes crosstalk matrix constant for a given sequencing run and that phasing affects all nucleotides in the same way

===General Noise Factors===
*Phasing
**Failures in nucleotide incorporation or block removal or incorporation of more than one nucleotide in a particular cycle
*Fading
**Decay in fluorescent signal intensity with each cycle
**Likely attributable to material loss during sequencing
*Crosstalk
**C channel illumination overlaps with A: a C label fluoresces in A channel (similarly G and T overlap)
**Likely caused by overlap in dye emission frequencies
*T Accumulation
**The fluorophores used for thymine are not always removed properly after each iteration
**Intensity of T signal increases across sequencing run

==Alta-Cyclic==
===Training Stage===
*Learns run-specific noise patterns according to model and finds optimized solution reducing affect of noise sources using a Support Vector Machine (SVM)
*Half of training set used for cross-validation

===Base Calling Stage===
*Reports all sequences from run with optimized parameters

===Differences from Standard Illumina Base Caller===
*Calling parameters optimized empirically and tested to enhance accuracy of each run
*Calculates phasing parameters based on parametric model
*Dynamically tracks changes in crosstalk, which disrupt signals in later cycles

==Probabilistic Base Calling==
*Produces an alternative probabilistic base calling method based on the fluorescence intensity quantifications that uses:
**Extended IUPAC alphabet to code ambiguous bases
**Information criterion to control length of trustable reads
*Reduced systematic bias by addressing:
**Crosstalk
**Dephasing
**Optical effect that tiles in center of image appear brighter corrected by fitting a 2D loess model to intensities and subtracting difference between fit and median intensities
*Measure level of uncertainty in base calling by entropy (uncertainty in determination of correct kth base)
*Does not consider fine-tuning image analysis

==BayesCall==
*Model-based approach to base calling
*Main goal is to model sequencing process by taking stochasticity into account and by explicitly modeling how errors may arise
*Obtain base calls by maximizing posterior distribution of sequences given observed data and assuming a uniform prior on sequences

==Swift==
Performs both image analysis and base calling

===Image Analysis===
*Background subtraction – minimal pixel value within a window around each pixel subtracted from central pixel’s value
*Image correlation – alignment of images to reference cycle
*Object identification and intensity extraction

===Base Calling===
*Corrects for crosstalk by performing linear regression on crosstalk plots and use slope to derive correction matrix, performed iteratively until slope is zero
*Phasing correction by ranking clusters by chastity (the ratio of the highest intensity to the sum of the top two intensities) - use top 400 clusters to estimate phasing and apply it as a correction
*After correction, base with maximum intensity chosen as called base

==Ibis==

===Method===
*Estimate sequencing chemistry model as a parameter directly from data using statistical learning
*Training set from Bustard output using raw cluster intensities
*Used a base caller with SVM classifiers for each cycle that have intensity values of the current cycle as well as the previous and following cycles (if they exist)
*Data set created by aligning raw reads with mismatches for a fraction of the tiles to a reference sequence
**Half of this set used as a training set and the other half as a test set used to check results of training
*Estimate parameters for calculating a quality score given class assignment and distances to the classification/decision boundary from SVM

===Comparison===
*Unlike AltaCyclic, includes base-specific phasing parameters so can correct raw intensities for T accumulation
*Does not call an 'N' character for poor quality bases
*Process unique as causes of sequencing error not modeled separately
**Consider causes together by using neighboring signals in statistical learning procedure

==References==

Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J. (2008) Alta-Cyclic: A self-optimizing base caller for next-generation sequencing. ''Nature Methods'' '''5''':679-682

Kao, W.-C., Stevens, K., Song, Y.S. (2009) BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing. ''Genome Research'' '''19''':1884-1895

Rougemont, J., Amzallag, A., Iseli, C., Farinelli, L., Xenarios, I., Naef, F. (2008) Probabilistic base calling of Solexa sequencing data. ''BMC Bioinformatics'' '''9''':Article 431

Whiteford, N., Skelly, T., Curtis, C., Ritchie, M.E., Löhr, A., Zaranek, A.W., Abnizova, I., Brown, C. (2009) Swift: Primary data analysis for the Illumina Solexa sequencing platform. ''Bioinformatics'' '''25''':2194-2199

Base Caller Summaries

2010-03-12T20:55:40Z

Srashkin: /* Ibis */

==Standard Illumina Base Caller (Bustard)==

===Sequencing-by-Synthesis (SBS)===

*DNA sample obtained, containing many copies of same sequences and randomly fragmented
*Single-stranded DNA fragments attached to slide and amplified so there is a cluster of each fragment
*DNA polymerase and 4 terminal bases (with distinct fluorescent markers) added
*Clusters excited by lasers and photos taken in optimal wavelengths for 4 fluorophores
*Fluorophores and terminators removed and process repeated for L cycles

===Image Analysis===
*Corrects for imperfect repositioning of camera and aberrations of lens by aligning images to reference from original cycle
*Signal for each cluster characterized as time series data of fluorescence intensities and noise

===Base Calling===
*Converts fluorescence signals into actual sequence data with quality scores
*Takes intensities of four channels for every cluster in each cycle and determines concentration of each base
*Renormalizes concentrations by multiplying by ratio of average concentrations in first cycle and current cycle
*Uses Markov model to determine transition matrix modeling probability of phasing (no new base synthesized), prephasing (two new bases synthesized), and normal incorporation
*Uses transition matrix and observed concentrations of each base to determine concentrations in absence of phasing and reports these as base calls
**Assumes crosstalk matrix constant for a given sequencing run and that phasing affects all nucleotides in the same way

===General Noise Factors===
*Phasing
**Failures in nucleotide incorporation or block removal or incorporation of more than one nucleotide in a particular cycle
*Fading
**Decay in fluorescent signal intensity with each cycle
**Likely attributable to material loss during sequencing
*Crosstalk
**C channel illumination overlaps with A: a C label fluoresces in A channel (similarly G and T overlap)
**Likely caused by overlap in dye emission frequencies
*T Accumulation
**The fluorophores used for thymine are not always removed properly after each iteration
**Intensity of T signal increases across sequencing run

==Alta-Cyclic==
===Training Stage===
*Learns run-specific noise patterns according to model and finds optimized solution reducing affect of noise sources using a Support Vector Machine (SVM)
*Half of training set used for cross-validation

===Base Calling Stage===
*Reports all sequences from run with optimized parameters

===Differences from Standard Illumina Base Caller===
*Calling parameters optimized empirically and tested to enhance accuracy of each run
*Calculates phasing parameters based on parametric model
*Dynamically tracks changes in crosstalk, which disrupt signals in later cycles

==Probabilistic Base Calling==
*Produces an alternative probabilistic base calling method based on the fluorescence intensity quantifications that uses:
**Extended IUPAC alphabet to code ambiguous bases
**Information criterion to control length of trustable reads
*Reduced systematic bias by addressing:
**Crosstalk
**Dephasing
**Optical effect that tiles in center of image appear brighter corrected by fitting a 2D loess model to intensities and subtracting difference between fit and median intensities
*Measure level of uncertainty in base calling by entropy (uncertainty in determination of correct kth base)
*Does not consider fine-tuning image analysis

==BayesCall==
*Model-based approach to base calling
*Main goal is to model sequencing process by taking stochasticity into account and by explicitly modeling how errors may arise
*Obtain base calls by maximizing posterior distribution of sequences given observed data and assuming a uniform prior on sequences

==Swift==
Performs both image analysis and base calling

===Image Analysis===
*Background subtraction – minimal pixel value within a window around each pixel subtracted from central pixel’s value
*Image correlation – alignment of images to reference cycle
*Object identification and intensity extraction

===Base Calling===
*Corrects for crosstalk by performing linear regression on crosstalk plots and use slope to derive correction matrix, performed iteratively until slope is zero
*Phasing correction by ranking clusters by chastity (the ratio of the highest intensity to the sum of the top two intensities) - use top 400 clusters to estimate phasing and apply it as a correction
*After correction, base with maximum intensity chosen as called base

==Ibis==

===Method===
*Estimate sequencing chemistry model as a parameter directly from data using statistical learning
*Training set from Bustard output using raw cluster intensities
*Used a base caller with SVM classifiers for each cycle that have intensity values of the current cycle as well as the previous and following cycles (if they exist)
*Data set created by aligning raw reads with mismatches for a fraction of the tiles to a reference sequence
**Half of this set used as a training set and the other half as a test set used to check results of training
*Estimate parameters for calculating a quality score given class assignment and distances to the classification/decision boundary from SVM

===Comparison===
*Unlike AltaCyclic, includes base-specific phasing parameters so can correct raw intensities for T accumulation
*Does not call an 'N' character for poor quality bases

==References==

Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J. (2008) Alta-Cyclic: A self-optimizing base caller for next-generation sequencing. ''Nature Methods'' '''5''':679-682

Kao, W.-C., Stevens, K., Song, Y.S. (2009) BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing. ''Genome Research'' '''19''':1884-1895

Rougemont, J., Amzallag, A., Iseli, C., Farinelli, L., Xenarios, I., Naef, F. (2008) Probabilistic base calling of Solexa sequencing data. ''BMC Bioinformatics'' '''9''':Article 431

Whiteford, N., Skelly, T., Curtis, C., Ritchie, M.E., Löhr, A., Zaranek, A.W., Abnizova, I., Brown, C. (2009) Swift: Primary data analysis for the Illumina Solexa sequencing platform. ''Bioinformatics'' '''25''':2194-2199

Base Caller Summaries

2010-03-12T20:53:07Z

Srashkin: /* Ibis */

==Standard Illumina Base Caller (Bustard)==

===Sequencing-by-Synthesis (SBS)===

*DNA sample obtained, containing many copies of same sequences and randomly fragmented
*Single-stranded DNA fragments attached to slide and amplified so there is a cluster of each fragment
*DNA polymerase and 4 terminal bases (with distinct fluorescent markers) added
*Clusters excited by lasers and photos taken in optimal wavelengths for 4 fluorophores
*Fluorophores and terminators removed and process repeated for L cycles

===Image Analysis===
*Corrects for imperfect repositioning of camera and aberrations of lens by aligning images to reference from original cycle
*Signal for each cluster characterized as time series data of fluorescence intensities and noise

===Base Calling===
*Converts fluorescence signals into actual sequence data with quality scores
*Takes intensities of four channels for every cluster in each cycle and determines concentration of each base
*Renormalizes concentrations by multiplying by ratio of average concentrations in first cycle and current cycle
*Uses Markov model to determine transition matrix modeling probability of phasing (no new base synthesized), prephasing (two new bases synthesized), and normal incorporation
*Uses transition matrix and observed concentrations of each base to determine concentrations in absence of phasing and reports these as base calls
**Assumes crosstalk matrix constant for a given sequencing run and that phasing affects all nucleotides in the same way

===General Noise Factors===
*Phasing
**Failures in nucleotide incorporation or block removal or incorporation of more than one nucleotide in a particular cycle
*Fading
**Decay in fluorescent signal intensity with each cycle
**Likely attributable to material loss during sequencing
*Crosstalk
**C channel illumination overlaps with A: a C label fluoresces in A channel (similarly G and T overlap)
**Likely caused by overlap in dye emission frequencies
*T Accumulation
**The fluorophores used for thymine are not always removed properly after each iteration
**Intensity of T signal increases across sequencing run

==Alta-Cyclic==
===Training Stage===
*Learns run-specific noise patterns according to model and finds optimized solution reducing affect of noise sources using a Support Vector Machine (SVM)
*Half of training set used for cross-validation

===Base Calling Stage===
*Reports all sequences from run with optimized parameters

===Differences from Standard Illumina Base Caller===
*Calling parameters optimized empirically and tested to enhance accuracy of each run
*Calculates phasing parameters based on parametric model
*Dynamically tracks changes in crosstalk, which disrupt signals in later cycles

==Probabilistic Base Calling==
*Produces an alternative probabilistic base calling method based on the fluorescence intensity quantifications that uses:
**Extended IUPAC alphabet to code ambiguous bases
**Information criterion to control length of trustable reads
*Reduced systematic bias by addressing:
**Crosstalk
**Dephasing
**Optical effect that tiles in center of image appear brighter corrected by fitting a 2D loess model to intensities and subtracting difference between fit and median intensities
*Measure level of uncertainty in base calling by entropy (uncertainty in determination of correct kth base)
*Does not consider fine-tuning image analysis

==BayesCall==
*Model-based approach to base calling
*Main goal is to model sequencing process by taking stochasticity into account and by explicitly modeling how errors may arise
*Obtain base calls by maximizing posterior distribution of sequences given observed data and assuming a uniform prior on sequences

==Swift==
Performs both image analysis and base calling

===Image Analysis===
*Background subtraction – minimal pixel value within a window around each pixel subtracted from central pixel’s value
*Image correlation – alignment of images to reference cycle
*Object identification and intensity extraction

===Base Calling===
*Corrects for crosstalk by performing linear regression on crosstalk plots and use slope to derive correction matrix, performed iteratively until slope is zero
*Phasing correction by ranking clusters by chastity (the ratio of the highest intensity to the sum of the top two intensities) - use top 400 clusters to estimate phasing and apply it as a correction
*After correction, base with maximum intensity chosen as called base

==Ibis==

*Estimate sequencing chemistry model as a parameter directly from data using statistical learning
*Training set from Bustard output using raw cluster intensities
*Used a base caller with SVM classifiers for each cycle that have intensity values of the current cycle as well as the previous and following cycles (if they exist)
*Data set created by aligning raw reads with mismatches for a fraction of the tiles to a reference sequence
**Half of this set used as a training set and the other half as a test set used to check results of training
*Estimate parameters for calculating a quality score given class assignment and distances to the classification/decision boundary from SVM

==References==

Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J. (2008) Alta-Cyclic: A self-optimizing base caller for next-generation sequencing. ''Nature Methods'' '''5''':679-682

Kao, W.-C., Stevens, K., Song, Y.S. (2009) BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing. ''Genome Research'' '''19''':1884-1895

Rougemont, J., Amzallag, A., Iseli, C., Farinelli, L., Xenarios, I., Naef, F. (2008) Probabilistic base calling of Solexa sequencing data. ''BMC Bioinformatics'' '''9''':Article 431

Whiteford, N., Skelly, T., Curtis, C., Ritchie, M.E., Löhr, A., Zaranek, A.W., Abnizova, I., Brown, C. (2009) Swift: Primary data analysis for the Illumina Solexa sequencing platform. ''Bioinformatics'' '''25''':2194-2199

Base Caller Summaries

2010-03-12T20:46:18Z

Srashkin: /* Standard Illumina Base Caller */

==Standard Illumina Base Caller (Bustard)==

===Sequencing-by-Synthesis (SBS)===

*DNA sample obtained, containing many copies of same sequences and randomly fragmented
*Single-stranded DNA fragments attached to slide and amplified so there is a cluster of each fragment
*DNA polymerase and 4 terminal bases (with distinct fluorescent markers) added
*Clusters excited by lasers and photos taken in optimal wavelengths for 4 fluorophores
*Fluorophores and terminators removed and process repeated for L cycles

===Image Analysis===
*Corrects for imperfect repositioning of camera and aberrations of lens by aligning images to reference from original cycle
*Signal for each cluster characterized as time series data of fluorescence intensities and noise

===Base Calling===
*Converts fluorescence signals into actual sequence data with quality scores
*Takes intensities of four channels for every cluster in each cycle and determines concentration of each base
*Renormalizes concentrations by multiplying by ratio of average concentrations in first cycle and current cycle
*Uses Markov model to determine transition matrix modeling probability of phasing (no new base synthesized), prephasing (two new bases synthesized), and normal incorporation
*Uses transition matrix and observed concentrations of each base to determine concentrations in absence of phasing and reports these as base calls
**Assumes crosstalk matrix constant for a given sequencing run and that phasing affects all nucleotides in the same way

===General Noise Factors===
*Phasing
**Failures in nucleotide incorporation or block removal or incorporation of more than one nucleotide in a particular cycle
*Fading
**Decay in fluorescent signal intensity with each cycle
**Likely attributable to material loss during sequencing
*Crosstalk
**C channel illumination overlaps with A: a C label fluoresces in A channel (similarly G and T overlap)
**Likely caused by overlap in dye emission frequencies
*T Accumulation
**The fluorophores used for thymine are not always removed properly after each iteration
**Intensity of T signal increases across sequencing run

==Alta-Cyclic==
===Training Stage===
*Learns run-specific noise patterns according to model and finds optimized solution reducing affect of noise sources using a Support Vector Machine (SVM)
*Half of training set used for cross-validation

===Base Calling Stage===
*Reports all sequences from run with optimized parameters

===Differences from Standard Illumina Base Caller===
*Calling parameters optimized empirically and tested to enhance accuracy of each run
*Calculates phasing parameters based on parametric model
*Dynamically tracks changes in crosstalk, which disrupt signals in later cycles

==Probabilistic Base Calling==
*Produces an alternative probabilistic base calling method based on the fluorescence intensity quantifications that uses:
**Extended IUPAC alphabet to code ambiguous bases
**Information criterion to control length of trustable reads
*Reduced systematic bias by addressing:
**Crosstalk
**Dephasing
**Optical effect that tiles in center of image appear brighter corrected by fitting a 2D loess model to intensities and subtracting difference between fit and median intensities
*Measure level of uncertainty in base calling by entropy (uncertainty in determination of correct kth base)
*Does not consider fine-tuning image analysis

==BayesCall==
*Model-based approach to base calling
*Main goal is to model sequencing process by taking stochasticity into account and by explicitly modeling how errors may arise
*Obtain base calls by maximizing posterior distribution of sequences given observed data and assuming a uniform prior on sequences

==Swift==
Performs both image analysis and base calling

===Image Analysis===
*Background subtraction – minimal pixel value within a window around each pixel subtracted from central pixel’s value
*Image correlation – alignment of images to reference cycle
*Object identification and intensity extraction

===Base Calling===
*Corrects for crosstalk by performing linear regression on crosstalk plots and use slope to derive correction matrix, performed iteratively until slope is zero
*Phasing correction by ranking clusters by chastity (the ratio of the highest intensity to the sum of the top two intensities) - use top 400 clusters to estimate phasing and apply it as a correction
*After correction, base with maximum intensity chosen as called base

==Ibis==

(To be added soon.)

==References==

Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J. (2008) Alta-Cyclic: A self-optimizing base caller for next-generation sequencing. ''Nature Methods'' '''5''':679-682

Kao, W.-C., Stevens, K., Song, Y.S. (2009) BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing. ''Genome Research'' '''19''':1884-1895

Rougemont, J., Amzallag, A., Iseli, C., Farinelli, L., Xenarios, I., Naef, F. (2008) Probabilistic base calling of Solexa sequencing data. ''BMC Bioinformatics'' '''9''':Article 431

Whiteford, N., Skelly, T., Curtis, C., Ritchie, M.E., Löhr, A., Zaranek, A.W., Abnizova, I., Brown, C. (2009) Swift: Primary data analysis for the Illumina Solexa sequencing platform. ''Bioinformatics'' '''25''':2194-2199

Base Caller Summaries

2010-03-12T18:26:51Z

Srashkin: /* General Noise Factors */

==Standard Illumina Base Caller==

===Sequencing-by-Synthesis (SBS)===

*DNA sample obtained, containing many copies of same sequences and randomly fragmented
*Single-stranded DNA fragments attached to slide and amplified so there is a cluster of each fragment
*DNA polymerase and 4 terminal bases (with distinct fluorescent markers) added
*Clusters excited by lasers and photos taken in optimal wavelengths for 4 fluorophores
*Fluorophores and terminators removed and process repeated for L cycles

===Image Analysis===
*Corrects for imperfect repositioning of camera and aberrations of lens by aligning images to reference from original cycle
*Signal for each cluster characterized as time series data of fluorescence intensities and noise

===Base Calling===
*Converts fluorescence signals into actual sequence data with quality scores
*Takes intensities of four channels for every cluster in each cycle and determines concentration of each base
*Renormalizes concentrations by multiplying by ratio of average concentrations in first cycle and current cycle
*Uses Markov model to determine transition matrix modeling probability of phasing (no new base synthesized), prephasing (two new bases synthesized), and normal incorporation
*Uses transition matrix and observed concentrations of each base to determine concentrations in absence of phasing and reports these as base calls

===General Noise Factors===
*Phasing
**Failures in nucleotide incorporation or block removal or incorporation of more than one nucleotide in a particular cycle
*Fading
**Decay in fluorescent signal intensity with each cycle
**Likely attributable to material loss during sequencing
*Crosstalk
**C channel illumination overlaps with A: a C label fluoresces in A channel (similarly G and T overlap)
**Likely caused by overlap in dye emission frequencies
*T Accumulation
**The fluorophores used for thymine are not always removed properly after each iteration
**Intensity of T signal increases across sequencing run

==Alta-Cyclic==
===Training Stage===
*Learns run-specific noise patterns according to model and finds optimized solution reducing affect of noise sources using a Support Vector Machine (SVM)
*Half of training set used for cross-validation

===Base Calling Stage===
*Reports all sequences from run with optimized parameters

===Differences from Standard Illumina Base Caller===
*Calling parameters optimized empirically and tested to enhance accuracy of each run
*Calculates phasing parameters based on parametric model
*Dynamically tracks changes in crosstalk, which disrupt signals in later cycles

==Probabilistic Base Calling==
*Produces an alternative probabilistic base calling method based on the fluorescence intensity quantifications that uses:
**Extended IUPAC alphabet to code ambiguous bases
**Information criterion to control length of trustable reads
*Reduced systematic bias by addressing:
**Crosstalk
**Dephasing
**Optical effect that tiles in center of image appear brighter corrected by fitting a 2D loess model to intensities and subtracting difference between fit and median intensities
*Measure level of uncertainty in base calling by entropy (uncertainty in determination of correct kth base)
*Does not consider fine-tuning image analysis

==BayesCall==
*Model-based approach to base calling
*Main goal is to model sequencing process by taking stochasticity into account and by explicitly modeling how errors may arise
*Obtain base calls by maximizing posterior distribution of sequences given observed data and assuming a uniform prior on sequences

==Swift==
Performs both image analysis and base calling

===Image Analysis===
*Background subtraction – minimal pixel value within a window around each pixel subtracted from central pixel’s value
*Image correlation – alignment of images to reference cycle
*Object identification and intensity extraction

===Base Calling===
*Corrects for crosstalk by performing linear regression on crosstalk plots and use slope to derive correction matrix, performed iteratively until slope is zero
*Phasing correction by ranking clusters by chastity (the ratio of the highest intensity to the sum of the top two intensities) - use top 400 clusters to estimate phasing and apply it as a correction
*After correction, base with maximum intensity chosen as called base

==Ibis==

(To be added soon.)

==References==

Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J. (2008) Alta-Cyclic: A self-optimizing base caller for next-generation sequencing. ''Nature Methods'' '''5''':679-682

Kao, W.-C., Stevens, K., Song, Y.S. (2009) BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing. ''Genome Research'' '''19''':1884-1895

Rougemont, J., Amzallag, A., Iseli, C., Farinelli, L., Xenarios, I., Naef, F. (2008) Probabilistic base calling of Solexa sequencing data. ''BMC Bioinformatics'' '''9''':Article 431

Whiteford, N., Skelly, T., Curtis, C., Ritchie, M.E., Löhr, A., Zaranek, A.W., Abnizova, I., Brown, C. (2009) Swift: Primary data analysis for the Illumina Solexa sequencing platform. ''Bioinformatics'' '''25''':2194-2199

Base Caller Summaries

2010-03-12T18:24:38Z

Srashkin: /* General Noise Factors */

==Standard Illumina Base Caller==

===Sequencing-by-Synthesis (SBS)===

*DNA sample obtained, containing many copies of same sequences and randomly fragmented
*Single-stranded DNA fragments attached to slide and amplified so there is a cluster of each fragment
*DNA polymerase and 4 terminal bases (with distinct fluorescent markers) added
*Clusters excited by lasers and photos taken in optimal wavelengths for 4 fluorophores
*Fluorophores and terminators removed and process repeated for L cycles

===Image Analysis===
*Corrects for imperfect repositioning of camera and aberrations of lens by aligning images to reference from original cycle
*Signal for each cluster characterized as time series data of fluorescence intensities and noise

===Base Calling===
*Converts fluorescence signals into actual sequence data with quality scores
*Takes intensities of four channels for every cluster in each cycle and determines concentration of each base
*Renormalizes concentrations by multiplying by ratio of average concentrations in first cycle and current cycle
*Uses Markov model to determine transition matrix modeling probability of phasing (no new base synthesized), prephasing (two new bases synthesized), and normal incorporation
*Uses transition matrix and observed concentrations of each base to determine concentrations in absence of phasing and reports these as base calls

===General Noise Factors===
*Phasing
**Failures in nucleotide incorporation or block removal or incorporation of more than one nucleotide in a particular cycle
*Fading
**Decay in fluorescent signal intensity with each cycle
**Likely attributable to material loss during sequencing
*Crosstalk
**C channel illumination overlaps with A: a C label fluoresces in A channel (similarly G and T overlap)
**Likely caused by overlap in dye emission frequencies
*T Accumulation
**The fluorophores used for thymine show lower removal rate after each iteration and accumulate over the sequencing run

==Alta-Cyclic==
===Training Stage===
*Learns run-specific noise patterns according to model and finds optimized solution reducing affect of noise sources using a Support Vector Machine (SVM)
*Half of training set used for cross-validation

===Base Calling Stage===
*Reports all sequences from run with optimized parameters

===Differences from Standard Illumina Base Caller===
*Calling parameters optimized empirically and tested to enhance accuracy of each run
*Calculates phasing parameters based on parametric model
*Dynamically tracks changes in crosstalk, which disrupt signals in later cycles

==Probabilistic Base Calling==
*Produces an alternative probabilistic base calling method based on the fluorescence intensity quantifications that uses:
**Extended IUPAC alphabet to code ambiguous bases
**Information criterion to control length of trustable reads
*Reduced systematic bias by addressing:
**Crosstalk
**Dephasing
**Optical effect that tiles in center of image appear brighter corrected by fitting a 2D loess model to intensities and subtracting difference between fit and median intensities
*Measure level of uncertainty in base calling by entropy (uncertainty in determination of correct kth base)
*Does not consider fine-tuning image analysis

==BayesCall==
*Model-based approach to base calling
*Main goal is to model sequencing process by taking stochasticity into account and by explicitly modeling how errors may arise
*Obtain base calls by maximizing posterior distribution of sequences given observed data and assuming a uniform prior on sequences

==Swift==
Performs both image analysis and base calling

===Image Analysis===
*Background subtraction – minimal pixel value within a window around each pixel subtracted from central pixel’s value
*Image correlation – alignment of images to reference cycle
*Object identification and intensity extraction

===Base Calling===
*Corrects for crosstalk by performing linear regression on crosstalk plots and use slope to derive correction matrix, performed iteratively until slope is zero
*Phasing correction by ranking clusters by chastity (the ratio of the highest intensity to the sum of the top two intensities) - use top 400 clusters to estimate phasing and apply it as a correction
*After correction, base with maximum intensity chosen as called base

==Ibis==

(To be added soon.)

==References==

Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J. (2008) Alta-Cyclic: A self-optimizing base caller for next-generation sequencing. ''Nature Methods'' '''5''':679-682

Kao, W.-C., Stevens, K., Song, Y.S. (2009) BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing. ''Genome Research'' '''19''':1884-1895

Rougemont, J., Amzallag, A., Iseli, C., Farinelli, L., Xenarios, I., Naef, F. (2008) Probabilistic base calling of Solexa sequencing data. ''BMC Bioinformatics'' '''9''':Article 431

Whiteford, N., Skelly, T., Curtis, C., Ritchie, M.E., Löhr, A., Zaranek, A.W., Abnizova, I., Brown, C. (2009) Swift: Primary data analysis for the Illumina Solexa sequencing platform. ''Bioinformatics'' '''25''':2194-2199

Base Caller Summaries

2010-02-26T16:09:44Z

Srashkin:

==Standard Illumina Base Caller==

===Sequencing-by-Synthesis (SBS)===

*DNA sample obtained, containing many copies of same sequences and randomly fragmented
*Single-stranded DNA fragments attached to slide and amplified so there is a cluster of each fragment
*DNA polymerase and 4 terminal bases (with distinct fluorescent markers) added
*Clusters excited by lasers and photos taken in optimal wavelengths for 4 fluorophores
*Fluorophores and terminators removed and process repeated for L cycles

===Image Analysis===
*Corrects for imperfect repositioning of camera and aberrations of lens by aligning images to reference from original cycle
*Signal for each cluster characterized as time series data of fluorescence intensities and noise

===Base Calling===
*Converts fluorescence signals into actual sequence data with quality scores
*Takes intensities of four channels for every cluster in each cycle and determines concentration of each base
*Renormalizes concentrations by multiplying by ratio of average concentrations in first cycle and current cycle
*Uses Markov model to determine transition matrix modeling probability of phasing (no new base synthesized), prephasing (two new bases synthesized), and normal incorporation
*Uses transition matrix and observed concentrations of each base to determine concentrations in absence of phasing and reports these as base calls

===General Noise Factors===
*Phasing
**Failures in nucleotide incorporation or block removal or incorporation of more than one nucleotide in a particular cycle
*Fading
**Decay in fluorescent signal intensity with each cycle
**Likely attributable to material loss during sequencing
*Crosstalk
**C channel illumination overlaps with A: a C label fluoresces in A channel (similarly G and T overlap)
**Likely caused by overlap in dye emission frequencies

==Alta-Cyclic==
===Training Stage===
*Learns run-specific noise patterns according to model and finds optimized solution reducing affect of noise sources using a Support Vector Machine (SVM)
*Half of training set used for cross-validation

===Base Calling Stage===
*Reports all sequences from run with optimized parameters

===Differences from Standard Illumina Base Caller===
*Calling parameters optimized empirically and tested to enhance accuracy of each run
*Calculates phasing parameters based on parametric model
*Dynamically tracks changes in crosstalk, which disrupt signals in later cycles

==Probabilistic Base Calling==
*Produces an alternative probabilistic base calling method based on the fluorescence intensity quantifications that uses:
**Extended IUPAC alphabet to code ambiguous bases
**Information criterion to control length of trustable reads
*Reduced systematic bias by addressing:
**Crosstalk
**Dephasing
**Optical effect that tiles in center of image appear brighter corrected by fitting a 2D loess model to intensities and subtracting difference between fit and median intensities
*Measure level of uncertainty in base calling by entropy (uncertainty in determination of correct kth base)
*Does not consider fine-tuning image analysis

==BayesCall==
*Model-based approach to base calling
*Main goal is to model sequencing process by taking stochasticity into account and by explicitly modeling how errors may arise
*Obtain base calls by maximizing posterior distribution of sequences given observed data and assuming a uniform prior on sequences

==Swift==
Performs both image analysis and base calling

===Image Analysis===
*Background subtraction – minimal pixel value within a window around each pixel subtracted from central pixel’s value
*Image correlation – alignment of images to reference cycle
*Object identification and intensity extraction

===Base Calling===
*Corrects for crosstalk by performing linear regression on crosstalk plots and use slope to derive correction matrix, performed iteratively until slope is zero
*Phasing correction by ranking clusters by chastity (the ratio of the highest intensity to the sum of the top two intensities) - use top 400 clusters to estimate phasing and apply it as a correction
*After correction, base with maximum intensity chosen as called base

==Ibis==

(To be added soon.)

==References==

Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J. (2008) Alta-Cyclic: A self-optimizing base caller for next-generation sequencing. ''Nature Methods'' '''5''':679-682

Kao, W.-C., Stevens, K., Song, Y.S. (2009) BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing. ''Genome Research'' '''19''':1884-1895

Rougemont, J., Amzallag, A., Iseli, C., Farinelli, L., Xenarios, I., Naef, F. (2008) Probabilistic base calling of Solexa sequencing data. ''BMC Bioinformatics'' '''9''':Article 431

Whiteford, N., Skelly, T., Curtis, C., Ritchie, M.E., Löhr, A., Zaranek, A.W., Abnizova, I., Brown, C. (2009) Swift: Primary data analysis for the Illumina Solexa sequencing platform. ''Bioinformatics'' '''25''':2194-2199

Base Caller Summaries

2010-02-26T16:04:50Z

Srashkin:

Base Caller Summaries

2010-02-26T16:01:54Z

Srashkin:

Base Caller Summaries

2010-02-26T15:49:38Z

Srashkin:

= Standard Illumina Base Caller =

= Alta-Cyclic =

= Probabilistic Base Calling =

= BayesCall =

= Swift =

= Ibis =

(To be added soon.)

= References =

Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J. (2008) Alta-Cyclic: A self-optimizing base caller for next-generation sequencing. ''Nature Methods'' '''5''':679-682

Kao, W.-C., Stevens, K., Song, Y.S. (2009) BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing. ''Genome Research'' '''19''':1884-1895

Rougemont, J., Amzallag, A., Iseli, C., Farinelli, L., Xenarios, I., Naef, F. (2008) Probabilistic base calling of Solexa sequencing data. ''BMC Bioinformatics'' '''9''':Article 431

Whiteford, N., Skelly, T., Curtis, C., Ritchie, M.E., Löhr, A., Zaranek, A.W., Abnizova, I., Brown, C. (2009) Swift: Primary data analysis for the Illumina Solexa sequencing platform. ''Bioinformatics'' '''25''':2194-2199

Base Caller Summaries

2010-02-26T15:45:19Z

Srashkin: Created page with '= Standard Illumina Base Caller = = Alta-Cyclic = = Probabilistic Base Calling = = BayesCall = = Swift = = IBIS = (To be added soon.) = References ='

= Standard Illumina Base Caller =

= Alta-Cyclic =

= Probabilistic Base Calling =

= BayesCall =

= Swift =

= IBIS =

(To be added soon.)

= References =

Links to Sequence Analysis Tools

2010-02-26T15:42:29Z

Srashkin: /* Base Calling Tools */

[[http://www.broadinstitute.org/gsa/wiki/index.php/Base_quality_score_recalibration Quality Score Calibration Tool]] at the Broad Institute.

== Base Calling Tools ==

[[Base Caller Summaries]]

Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J. (2008) Alta-Cyclic: A self-optimizing base caller for next-generation sequencing. ''Nature Methods'' '''5''':679-682

Rougemont, J., Amzallag, A., Iseli, C., Farinelli, L., Xenarios, I., Naef, F. (2008) Probabilistic base calling of Solexa sequencing data. ''BMC Bioinformatics'' '''9''':Article 431

Kao, W.-C., Stevens, K., Song, Y.S. (2009) BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing. ''Genome Research'' '''19''':1884-1895

Whiteford, N., Skelly, T., Curtis, C., Ritchie, M.E., Löhr, A., Zaranek, A.W., Abnizova, I., Brown, C. (2009)
Swift: Primary data analysis for the Illumina Solexa sequencing platform. ''Bioinformatics'' '''25''':2194-2199