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Open AccessJournal ArticleDOI

Tools for mapping high-throughput sequencing data

TLDR
This survey focuses on classifying mappers through a wide number of characteristics to allow practitioners to compare the mappers more easily and find those that are most suitable for their specific problem.
Abstract
Motivation: A ubiquitous and fundamental step in high-throughput sequencing analysis is the alignment (mapping) of the generated reads to a reference sequence. To accomplish this task, numerous software tools have been proposed. Determining the mappers that are most suitable for a specific application is not trivial. Results: This survey focuses on classifying mappers through a wide number of characteristics. The goal is to allow practitioners to compare the mappers more easily and find those that are most suitable for their specific problem. Availability: A regularly updated compendium of mappers can be found at http://wwwdev.ebi.ac.uk/fg/hts_mappers/. Contact: [email protected] Supplementary information:Supplementary data are available at Bioinformatics online.

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Citations
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featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features

TL;DR: FeatureCounts as discussed by the authors is a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments, which implements highly efficient chromosome hashing and feature blocking techniques.
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Detecting and characterizing circular RNAs

TL;DR: Evidence is emerging that some circRNAs might regulate microRNA (miRNA) function, and roles in transcriptional control have also been suggested.
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Fast and sensitive taxonomic classification for metagenomics with Kaiju

TL;DR: This work presents the novel metagenome classifier Kaiju, which finds maximum (in-)exact matches on the protein-level using the Burrows–Wheeler transform, and shows in a genome exclusion benchmark that Kaiju classifies reads with higher sensitivity and similar precision compared with current k-mer-based classifiers.
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Computational and analytical challenges in single-cell transcriptomics

TL;DR: The development of high-throughput RNA sequencing at the single-cell level has already led to profound new discoveries in biology, ranging from the identification of novel cell types to the study of global patterns of stochastic gene expression.
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Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

TL;DR: This protocol presents a state-of-the-art computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and, in particular, on two widely used tools, DESeq and edgeR.
References
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Journal ArticleDOI

The Sequence Alignment/Map format and SAMtools

TL;DR: SAMtools as discussed by the authors implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
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Fast and accurate short read alignment with Burrows–Wheeler transform

TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
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Fast gapped-read alignment with Bowtie 2

TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

TL;DR: Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches and can be used simultaneously to achieve even greater alignment speeds.
Journal ArticleDOI

RNA-Seq: a revolutionary tool for transcriptomics

TL;DR: The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.
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