Optimal spliced alignments of short sequence reads

Bona, Fabio; Ossowski, Stephan; Schneeberger, Korbinian; Rätsch, Gunnar

doi:10.1093/bioinformatics/btn300

Cited by 90 publications

(55 citation statements)

References 22 publications

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“…To use these reads for tasks such as transcriptome sequencing and gene structure identification the sequence reads must be aligned over intron boundaries [23]. When a reference genome assembly exists, alignment is performed.…”

Section: A Genomic Sequence Mappingmentioning

confidence: 99%

Biomarker discovery, high performance and cloud computing: A comprehensive review

Blayney

Haberland

Lightbody

et al. 2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Abstract-The analysis of biological markers (biomarkers) have the ability to improve clinical outcomes of a disease through prediction and early detection. The constant improvement of next generation sequencing (NGS) technologies coupled with falling equipment price are driving research and application especially in the medical domain. NGS technology is being applied to cancer research promising greater understanding of carcinogenesis. However, as sequence capacity grows, algorithmic speed is becoming an important bottleneck. To understand these challenges we present a review on difficulties currently faced in discovering biomarkers. The review places the spotlight on sequencing technologies and the bottlenecks encountered in these pipelines. Cloud computing and high performance computing technologies such as Grid are summarized in tackling computational challenges presented by technologies and algorithms used in biomarker research.

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Section: A Genomic Sequence Mappingmentioning

confidence: 99%

Biomarker discovery, high performance and cloud computing: A comprehensive review

Blayney

Haberland

Lightbody

et al. 2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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show abstract

“…Currently, alternative re-sequencing approaches use multiple modules in a serial pipeline (i.e., without feedback) to interpret raw sequencing data from next-generation sequencing platforms, while remaining oblivious to the genomic information until the final alignment step [5,12,26,24,23,17,3]. Such approaches fail to exploit the full information from both raw sequencing data and the reference genome that can yield better quality sequence reads, SNP-calls, variant detection, as well as an alignment at the best possible location in the reference genome.…”

Section: Totalrecaller: Base-calling Innovationmentioning

confidence: 99%

“…3 Various versions of the "Stringomics," can be created using basic building blocks for: D K (to keep track of the indexing), T K (to organize the underlying strings and stringlets) and P K (to perform 2d-range queries in an index-space).…”

Section: Base-calling With Alignment To An Annotated Reference Genomementioning

confidence: 99%

Gappy TotalReCaller for RNASeq Base-Calling and Mapping

Mishra

2013

Preprint

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Understanding complex mammalian biology depends crucially on our ability to define a precise map of all the transcripts encoded in a genome, and to measure their relative abundances. A promising assay depends on RNASeq approaches, which builds on next generation sequencing pipelines capable of interrogating cDNAs extracted from a cell. The underlying pipeline starts with base-calling, collect the sequence reads and interpret the raw-read in terms of transcripts that are grouped with respect to different splice-variant isoforms of a messenger RNA. We address a very basic problem involved in all of these pipeline, namely accurate Bayesian base-calling, which could combine the analog intensity data with suitable underlying priors on base-composition in the transcripts. In the context of sequencing genomic DNA, a powerful approach for base-calling has been developed in the TotalReCaller pipeline. For these purposes, it uses a suitable reference whole-genome sequence in a compressed self-indexed format to derive its priors. However, TotalReCaller faces many new challenges in the transcriptomic domain, especially since we still lack a fully annotated library of all possible transcripts, and hence a sufficiently good prior. There are many possible solutions, similar to the ones developed for TotalReCaller, in applications addressing de novo sequencing and assembly, where partial contigs or string-graphs could be used to boot-strap the Bayesian priors on base-composition. A similar approach would be applicable here too, partial assembly of transcripts can be used to characterize the splicing junctions or organize them in incompatibility graphs and then used as priors for TotalReCaller. The key algorithmic techniques for this purpose have been addressed in a forthcoming paper on Stringomics. Here, we address a related but fundamental problem, by assuming that we only have a reference genome, with certain intervals marked as candidate regions for ORF (Open Reading Frames), but not necessarily complete annotations regarding the 5' or 3' termini of a gene or its exon-intron structure. The algorithms we describe find the most accurate base-calls of a cDNA with the best possible segmentation, all mapped to the genome appropriately.

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“…Additionally, we used the same number of genes with only a single annotated transcripts. We aligned the reads of length 36 using Palmapper [26,27] and obtained 39.9 million and 42.3 million alignments for the two samples, where we only used the best alignment of each read.…”

Section: Detection Of Differentially Expressed Transcripts In C Elegansmentioning

confidence: 99%

Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts

et al. 2010

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As a fruit of the current revolution in sequencing technology, transcriptomes can now be analyzed at an unprecedented level of detail. These advances have been exploited for detecting differential expressed genes across biological samples and for quantifying the abundances of various RNA transcripts within one gene. However, explicit strategies for detecting the hidden differential abundances of RNA transcripts in biological samples have not been defined. In this work, we present two novel statistical tests to address this issue: a 'gene structure sensitive' Poisson test for detecting differential expression when the transcript structure of the gene is known, and a kernel-based test called Maximum Mean Discrepancy when it is unknown. We analyzed the proposed approaches on simulated read data for two artificial samples as well as on factual reads generated by the Illumina Genome Analyzer for two C. elegans samples. Our analysis shows that the Poisson test identifies genes with differential transcript expression considerably better that previously proposed RNA transcript quantification approaches for this task. The MMD test is able to detect a large fraction (75%) of such differential cases without the knowledge of the annotated transcripts. It is therefore well-suited to analyze RNA-Seq experiments when the genome annotations are incomplete or not available, where other approaches have to fail.

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Optimal spliced alignments of short sequence reads

Abstract: Datasets for training and evaluation, additional results and a stand-alone alignment tool implemented in C++ and python are available at http://www.fml.mpg.de/raetsch/projects/qpalma.

Cited by 90 publications

References 22 publications

Biomarker discovery, high performance and cloud computing: A comprehensive review

Biomarker discovery, high performance and cloud computing: A comprehensive review

Gappy TotalReCaller for RNASeq Base-Calling and Mapping

Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts

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