Please note: TopHat has a number of parameters and options, and their default values are tuned for processing mammalian RNA-Seq reads. If you would like to use TopHat for another class of organism, we recommend setting some of the parameters with more strict, conservative values than their defaults. Usually, setting the maximum intron size to 4 or 5 Kb is sufficient to discover most junctions while keeping the number of false positives low.
Administrator: Daehwan Kim. Design by David Herreman. Source code. The basename of the genome index to be searched. The basename is the name of any of the index files up to but not including the first period. Final read alignments having more than these many mismatches are discarded.
The default is 2. Final read alignments having more than these many total length of gaps are discarded. Final read alignments having more than these many edit distance are discarded. Some of the reads spanning multiple exons may be mapped incorrectly as a contiguous alignment to the genome even though the correct alignment should be a spliced one - this can happen in the presence of processed pseudogenes that are rarely if at all transcribed or expressed. This option can direct TopHat to re-align reads for which the edit distance of an alignment obtained in a previous mapping step is above or equal to this option value.
If you set this option to 0 , TopHat will map every read in all the mapping steps transcriptome if you provided gene annotations, genome, and finally splice variants detected by TopHat , reporting the best possible alignment found in any of these mapping steps. This may greatly increase the mapping accuracy at the expense of an increase in running time. The default value for this option is set such that TopHat will not try to realign reads already mapped in earlier steps.
Uses Bowtie1 instead of Bowtie2. If you use colorspace reads, you need to use this option as Bowtie2 does not support colorspace reads. Sets the name of the directory in which TopHat will write all of its output.
The default is ". This is the expected mean inner distance between mate pairs. For, example, for paired end runs with fragments selected at bp, where each end is 50bp, you should set -r to be The default is 50bp. The standard deviation for the distribution on inner distances between mate pairs. The default is 20bp. The "anchor length". TopHat will report junctions spanned by reads with at least this many bases on each side of the junction. Note that individual spliced alignments may span a junction with fewer than this many bases on one side.
However, every junction involved in spliced alignments is supported by at least one read with this many bases on each side. This must be at least 3 and the default is 8. The maximum number of mismatches that may appear in the "anchor" region of a spliced alignment. The default is 0. The minimum intron length.
The default is The maximum intron length. As of the Illumina GA pipeline version 1. Colorspace reads, note that it uses a colorspace bowtie index and requires Bowtie 0. Instructs TopHat to allow up to this many alignments to the reference for a given read, and choose the alignments based on their alignment scores if there are more than this number. The default is 20 for read mapping. Unless you use --report-secondary-alignments, TopHat will report the alignments with the best alignment score.
This must be at least 3 and the default is 8. The default is 0. The default is TopHat will use the exon records in this file to build a set of known splice junctions for each gene, and will attempt to align reads to these junctions even if they would not normally be covered by the initial mapping. Junctions are specified one per line, in a tab-delimited format. Use when coverage search is disabled by default such as for reads 75bp or longer , for maximum sensitivity.
Works only for reads 50bp or longer. The default is 2. These segments are mapped independently. TopHat reported The remaining 19 may represent novel junctions. For genes transcribed above TopHat detects junctions in genes transcribed at very low levels. The gene Pnlip was transcribed at only 7. To assess TopHat's ability to identify true junctions without reporting false positives, we simulated the results of Illumina short-read sequencing of alternatively spliced genes at several depths.
These were generated by the short-read simulator from Maq. The simulator computes an empirical distribution of read quality scores and uses these to generate sequencing errors in the reads it produces.
We trained the simulator using the reads from the Mortazavi et al. We generated simulated sequence from the ASTD transcripts, which contained splice junctions, at 1-, 5-, , and fold coverage. TopHat's junction predictions at each coverage level are summarized in Table 1.
Sensitivity suffers when transcripts are sequenced at less than 5-fold coverage. TopHat reports few false positives even in deeply sequenced transcripts. The simulation sampled a set of transcripts with true splice junctions. We also searched the database for known junctions and randomly generated junctions as positive and negative controls, respectively. The positive control group was drawn from the junction sequences constructed by Mortazavi et al.
The second set consisted of previously unreported junction sequences reported by TopHat. The negative control consisted of random pairings of the left and right halves of junction sequences from the second group. All sequences in each of the three groups were 42 bp long, and each group contained sequences chosen randomly. As expected, nearly all of the known junctions are confirmed by high-quality hits to ESTs. Randomly-generated junction sequences do not.
We examined the previously unreported junctions that lacked high-quality hits to mouse EST by dividing them into three categories: junctions between two known exons, junctions between a known exon and a novel one and junctions between two novel exons.
Of the 17 junctions without EST hits, 10 joined novel exons, joined a novel exon with a known one and joined a pair of known exons. One example of a junction from the second category is occurred in the ADP-ribosylation factor Arfgef1 , which is important in vesicular trafficking Morinaga et al. The junction in Figure 7 skips two of the gene's 38 exons. TopHat reported several junctions in Arfgef1 that were previously unknown and indicates that Arfgef1 is alternatively spliced.
A previously unreported splice junction detected by TopHat is shown as the topmost horizontal line. This junction skips two exons in the ADP-ribosylation gene Arfgef1. As explained in Section 2 , islands of read coverage in the Bowtie mapping are extended by 45 bp on either side. The advantage of such a strategy is that, like TopHat, no known junctions or gene models are needed.
We ran the Velvet short-read assembler Zerbino and Birney, version 0. We speculate that many of these highly transcribed genes have several alternate isoforms, and that junctions in these genes may cause Velvet to break contigs at the transcript junctions shared by multiple isoforms. The entire TopHat run took 21 h, 50 min on a 3.
More significant is its ability to detect novel splice junctions. While it is difficult to assess how many of TopHat's 19 newly discovered junctions are genuine, TopHat's alignment parameters for this run were quite strict: only exact matches were reported for splice junctions, and reads were required to have relatively long anchors on each side of the splice site. Close inspection of junctions strengthened the case that many are true splices. The TopHat pipeline processed an entire RNA-Seq run in less than a day on a single processor of a standard workstation.
ERANGE is appropriate for high-quality measurement of gene expression in mammalian RNA-Seq projects, provided that a reliable annotation of exon—exon junctions is available. QPALMA can accurately align short reads across junctions without an annotation, but makes such substantial sacrifices in speed that it may not be practical for large mammalian projects. TopHat thus represents a significant advance over previous RNA-Seq splice detection methods, both in its performance and its ability to find junctions de novo.
The TopHat pipeline and its default parameter values are designed for detecting junctions even in genes transcribed at very low levels.
However, the system may fail to detect junctions for a variety of reasons. The most common reason for missing a junction is that the transcript has very low sequencing coverage, in which case there might be no read that straddles the junction with sufficient sequence on each side.
Junctions spanning very long introns or introns with non-canonical donor and acceptor sites such as GC—AG introns will also be missed. As discussed in Section 2 , TopHat can also miss single-island junctions in islands with a low normalized depth of coverage. Single-island junctions can occur when the UTR of one isoform entirely overlaps an intron from another isoform, as illustrated in Figure 2. They may also occur when a transcript is incompletely processed.
While several thousand known junctions were captured by TopHat but not reported by ERANGE, this merely reflects differences in the goal of the two programs. For reads with multiple spliced alignments, ERANGE assigns each read to a single position, in order to increase the accuracy of its expression estimates. Were TopHat to do this, its sensitivity would suffer slightly. Alternatively, if annotations are unavailable or incomplete, then we recommend using TopHat2 with its realignment algorithm to produce the most complete set of alignments.
The run time and the peak memory usage of the programs used in this study varied greatly. We compared performance on all programs using the Chen et al. Overall, STAR is much faster 32 minutes than the other programs, which required from 8 to 55 hours. The Ensembl gene annotations release 66 contain 32, genes, including non-coding RNA genes, and over 14, pseudogenes. Of the real genes, we found that 2. Using data from the Chen et al.
Table 5 shows the proportion of reads mapping to genes with pseudogenes, using both the raw count and a normalized count divided by the length of the transcript. Although only 2.
From both RNA-seq experiments, we noted that genes with multiple pseudogene copies were more abundantly expressed than those with a single pseudogene copy. Figure 4 shows various mapping results from TopHat2 with and without realignments at various edit distances. As we allowed TopHat2 to realign more reads, it found the spliced alignments that were otherwise hidden by pseudogene alignments.
This in turn substantially increased its mapping rates for known splice sites. The number of read and spliced-read alignments from TopHat2, using different realignment edit distances and no realignment.
Edit distances of 0, 1, and 2 were used. As TopHat2 allows more realignment from no realignment to 2 to 1 to 0, the number of read alignments and spliced-read alignments increases, so that the differences in the numbers of read alignments from TopHat run with different realignment edit distance are mostly explained by the increase in the number of spliced-read alignments.
Although this analysis considered only the RNA-seq data from Chen et al. Most of the novel splicing events in these alignments were supported by 10 or more reads that extended for 50 or more bases on each side.
The number of read alignments whose splice sites were found in the gene annotations are shown in brown, and the number of all spliced-read alignments including novel splice sites are shown in green.
Discovery of new genes and transcripts is a major objective in many RNA-seq experiments. Deep RNA-seq experiments continue to uncover previously unseen elements of the transcriptome, even in well-studied organisms. Mapping reads to the genome is a core step in such screens, and the accuracy of mapping software can determine the accuracy of downstream steps such as gene and transcript discovery or expression quantification.
We have described TopHat2, which provides major accuracy improvements over previous versions and over other RNA-seq mapping tools. Because TopHat2 is built around Bowtie2, it can now align reads across small indels with high accuracy, a feature crucial for studies assessing the effects of genetic mutations on gene and transcript expression.
TopHat2 is engineered to work well with a wide range of RNA-seq experimental designs, and it is optimized for the widely available long paired-end reads. These reads pose new challenges because they can span multiple splice sites rather than just one or two; we estimate that nearly half of reads bp long will span two or more human exons.
The algorithmic improvements in TopHat2 address this challenge, maintaining both accuracy and speed. TopHat2 also makes powerful use of available gene annotations, which allow it to avoid erroneously mapping reads to pseudogenes, and generally improve its overall alignment accuracy. Annotation also allows TopHat2 to better align reads that cover microexons, non-canonical splice sites, and other 'unusual' features of eukaryotic transcriptomes.
We have shown that TopHat2 performs well over a wide range of read lengths, making it a good fit for most RNA-seq experimental designs. This scalability suggests that as read lengths grow, TopHat2 will continue to report accurate, sensitive alignment results and allow for robust downstream analysis.
We believe that TopHat2 reports more accurate alignments than competing tools, using fewer computational resources. RNA-seq experiments are becoming increasingly common and are now routinely used by many biologists. We expect that TopHat2 will provide these scientists with accurate results for use with expression analysis, gene discovery, and many other applications.
Given RNA-seq reads as input, TopHat2 begins by mapping reads against the known transcriptome, if an annotation file is provided. This transcriptome mapping improves the overall sensitivity and accuracy of the mapping. It also gives the whole pipeline a significant speed increase, owing to the much smaller size of the transcriptome compared with that of the genome see Figure 6.
After the transcriptome-mapping step, some reads remain unmapped because they are derived from unknown transcripts not present in the annotation, or because they contain many miscalled bases. In addition, there may be poorly aligned reads that have been mapped to the wrong location. TopHat2 aligns these unmapped or potentially misaligned reads against the genome Figure 6 , step 2. Any reads contained entirely within exons will be mapped, whereas other spanning introns may not be.
TopHat2 also provides an option to allow users to remap some of the mapped reads, depending on the edit distance values of these reads; that is, those reads whose edit distance is greater than or equal to a user-provided threshold will be treated as unmapped reads. To accomplish this, the unmapped reads and previously mapped reads with low alignment scores are split into smaller non-overlapping segments 25 bp each by default which are then aligned against the genome Figure 6 , step 3.
Tophat2 examines any cases in which the left and right segments of the same read are mapped within a user-defined maximum intron size usually between 50 and , bp. When this pattern is detected, TopHat2 re-aligns the entire read sequence to that genomic region in order to identify the most likely locations of the splice sites Figure 6. Using a similar approach, indels and fusion breakpoints are also detected in this step. The genomic sequences flanking these splice sites are concatenated, and the resulting spliced sequences are collected as a set of potential transcript fragments.
Any reads not mapped in the previous stages or mapped very poorly are then re-aligned with Bowtie2 [ 15 ] against this novel transcriptome. After these steps, some of the reads may have been aligned incorrectly by extending an exonic alignment a few bases into the adjacent intron Figure 1 ; Figure 6 , steps 3 to 5.
TopHat2 checks if such alignments extend into the introns identified in the split-alignment phase; if so, it can realign these reads to the adjacent exons instead. In the final stage, TopHat2 divides the reads into those with unique alignments and those with multiple alignments. For the multi-mapped reads, TopHat2 gathers statistical information for example, the number of supporting reads about the relevant splice junctions, insertions, and deletions, which it uses to recalculate the alignment score for each read.
Based on these new alignment scores, TopHat2 reports the most likely alignment locations for such multi-mapped reads. For paired-end reads, TopHat2 processes the two reads separately through the same mapping stages described above. In the final stage, the independently aligned reads are analyzed together to produce paired alignments, taking into consideration additional factors including fragment length and orientation.
For the experiments described in this study, the program version numbers were: TopHat2 2.
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