Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu. RationaleImprovements in the efficiency of DNA sequencing have both broadened the applications for sequencing and dramatically increased the size of sequencing datasets. Technologies from Illumina (San Diego, CA, USA) and Applied Biosystems (Foster City, CA, USA) have been used to profile methylation patterns (MeDIP-Seq) [1], to map DNA-protein interactions (ChIP-Seq) [2], and to identify differentially expressed genes (RNA-Seq) [3] in the human genome and other species. The Illumina instrument was recently used to re-sequence three human genomes, one from a cancer patient and two from previously unsequenced ethnic groups [4][5][6]. Each of these studies required the alignment of large numbers of short DNA sequences ('short reads') onto the human genome. For example, two of the studies [4,5] used the short read alignment tool Maq [7] to align more than 130 billion bases (about 45× coverage) of short Illumina reads to a human reference genome in order to detect genetic variations. The third human resequencing study [6] used the SOAP program [8] to align more than 100 billion bases to the reference genome. In addition to these projects, the 1,000 Genomes project is in the process of using high-throughput sequencing instruments to sequence a total of about six trillion base pairs of human DNA [9].With existing methods, the computational cost of aligning many short reads to a mammalian genome is very large. For example, extrapolating from the results presented here in Tables 1 and 2, one can see that Maq would require more than 5 central processing unit (CPU)-months and SOAP more than 3 CPU-years to align the 140 billion bases from the study by Ley and coworkers [5]. Although using Maq or SOAP for this purpose has been shown to be feasible by using multiple CPUs, there is a clear need for new tools that consume less time and computational resources.Maq and SOAP take the same basic algorithmic approach as other recent read mapping tools such as RMAP [10], ZOOM [11], and SHRiMP [12]. Each tool builds a hash table of short oligomers present in either the reads (SHRiMP, Maq, RMAP, and ZOOM) or the reference (SOAP). Some employ recent theoretical advances to align reads quickly without sacrificing sensitivity. For example, ZOOM uses 'spaced seeds' to significantly outperform RMAP, which is based on a simpler algo-

High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation1-3. We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.

Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ~1 h of hands-on time.