Swift: primary data analysis for the Illumina Solexa sequencing platform

Whiteford, Nava; Skelly, Tom; Curtis, Christina; Ritchie, Matthew E.; Löhr, Andrea; Zaranek, Alexander Wait; Abnizova, Irina; Brown, Clive

doi:10.1093/bioinformatics/btp383

Cited by 88 publications

(68 citation statements)

References 13 publications

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“…In the present study, we also observed that UMI logic eliminates the so-called optical duplicates (42), that is, cases when a single DNA cluster (raw Illumina instrument signal that is processed into a sequencing read) is misidentified as a group of several identical DNA clusters. Such duplicates could be clearly detected as a peak of read pairs that carry the same UMI with the intercluster distance ,100 pixels (Fig.…”

Section: Revising Umi-based Analysissupporting

confidence: 60%

Quantitative Profiling of Immune Repertoires for Minor Lymphocyte Counts Using Unique Molecular Identifiers

Egorov

Merzlyak

Shelenkov

et al. 2015

The Journal of Immunology

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Emerging high-throughput sequencing methods for the analyses of complex structure of TCR and BCR repertoires give a powerful impulse to adaptive immunity studies. However, there are still essential technical obstacles for performing a truly quantitative analysis. Specifically, it remains challenging to obtain comprehensive information on the clonal composition of small lymphocyte populations, such as Ag-specific, functional, or tissue-resident cell subsets isolated by sorting, microdissection, or fine needle aspirates. In this study, we report a robust approach based on unique molecular identifiers that allows profiling Ag receptors for several hundred to thousand lymphocytes while preserving qualitative and quantitative information on clonal composition of the sample. We also describe several general features regarding the data analysis with unique molecular identifiers that are critical for accurate counting of starting molecules in high-throughput sequencing applications.

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Section: Revising Umi-based Analysissupporting

confidence: 60%

Quantitative Profiling of Immune Repertoires for Minor Lymphocyte Counts Using Unique Molecular Identifiers

Egorov

Merzlyak

Shelenkov

et al. 2015

The Journal of Immunology

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“…There are two main approaches to addressing this challenge: (1) One approach is to develop improved image analysis and base-calling algorithms. This line of work has been pursued by several researchers in the past, including ourselves (for review, see Erlich et al 2008;Rougemont et al 2008;Kao et al 2009;Kircher et al 2009;Whiteford et al 2009; Kao and Song 2011). Indeed, by using more sophisticated statistical methods, it has been demonstrated that it is possible to deliver significant improvements over the tools developed by the manufacturers of the sequencing platforms.…”

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confidence: 99%

ECHO: A reference-free short-read error correction algorithm

Kao

Chan²,

Song

2011

Genome Res.

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Developing accurate, scalable algorithms to improve data quality is an important computational challenge associated with recent advances in high-throughput sequencing technology. In this study, a novel error-correction algorithm, called ECHO, is introduced for correcting base-call errors in short-reads, without the need of a reference genome. Unlike most previous methods, ECHO does not require the user to specify parameters of which optimal values are typically unknown a priori. ECHO automatically sets the parameters in the assumed model and estimates error characteristics specific to each sequencing run, while maintaining a running time that is within the range of practical use. ECHO is based on a probabilistic model and is able to assign a quality score to each corrected base. Furthermore, it explicitly models heterozygosity in diploid genomes and provides a reference-free method for detecting bases that originated from heterozygous sites. On both real and simulated data, ECHO is able to improve the accuracy of previous error-correction methods by several folds to an order of magnitude, depending on the sequence coverage depth and the position in the read. The improvement is most pronounced toward the end of the read, where previous methods become noticeably less effective. Using a wholegenome yeast data set, it is demonstrated here that ECHO is capable of coping with nonuniform coverage. Also, it is shown that using ECHO to perform error correction as a preprocessing step considerably facilitates de novo assembly, particularly in the case of low-to-moderate sequence coverage depth.[Supplemental material is available for this article. ECHO is publicly available at http://uc-echo.sourceforge.net under the Berkeley Software Distribution License.]Over the past few years, next-generation sequencing (NGS) technologies have introduced a rapidly growing wave of information in biological sciences; see Metzker (2010) for a recent review of NGS platforms and their applications. Exploiting massive parallelization, NGS platforms generate high-throughput data at very low cost per base. An important computational challenge associated with this rapid technological advancement is to develop efficient algorithms to extract accurate sequence information. In comparison with traditional Sanger sequencing (Sanger et al. 1977), NGS data have shorter read lengths and higher error rates, and these characteristics create many challenges for computation, especially when a reference genome is not available. Reducing the error rate of base-calls and improving the accuracy of base-specific quality scores have important practical implications for assembly (Sundquist et al.

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“…In this case (and in case when heterogeneous data are merged) it is recommended to re-calibrate the data using formula (1) [146,149,150]. There is in-house Sanger Institute recalibration and error analysis implemented [30].…”

Section: Box 10mentioning

confidence: 99%