Bowtie: cannot read fasta file?

Bowtie: cannot read fasta file?

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I am trying to usebowtie2to analyze my data in FASTA format, but it seems that this version can't read properly my data. My command line is as follows:

bowtie2 -x $REFERENCE -f $TARGET -S $TARGET.sam

The version 2 of bowtie complains with the following:

Warning: skipping read 'ORIGINAL: GACACTGTTCATGCTGGTGTCGCTGTCGGGCATTAT' because length (0) <= # seed mismatches (0) Warning: skipping read 'ORIGINAL: GACACTGTTCATGCTGGTGTCGCTGTCGGGCATTAT' because it was < 2 characters long Warning: skipping read 'ORIGINAL: GGCTATCTTGAAGCCAATGAGTTGTTAACTGGCAAG' because length (0) <= # seed mismatches (0)

Note thatbowtie(version 1) is pleased by my FASTA! Here's a snippet and whatbowtiesays:


Now I'm at loss. Can anyone see what I'm doing wrong here? Can I trust anywaybowtie2when it finishes and outputs how many sequences are aligned?


Since I apparently don't have enough reputation to comment, I'll post this as an answer and let someone move it.

Firstly, try to ensure that the order of the arguments are correct. You should be typingbowtie2 -f -x $TARGET -U $TARGET -S $TARGET.sam, since bowtie2 (as with many other programs) can be a bit picky about the argument order.

Secondly, it's usually advisable for the example input to include lines that are causing the problem (since it's apparently not complaining about the lines you posted, we can only assume that it's aligning them).

Thirdly, you'll typically only get those warnings if they're true. For example, if you were togrep -A 1 -w GACACTGTTCATGCTGGTGTCGCTGTCGGGCATTAT $targetthen my guess is that you'd find it to have no remaining sequence. Presumably these are the result of you trimming adapters or something like that, so just have your trimmer discard really short results (they won't align meaningfully anyway).

Aligning the reads to the transcriptome

Armed with the clustered fasta file and the trimmed raw reads you can now determine the read hits per transcript.

Alignment functions are supported within the Trinity platform, thereby incorporating the use of these third party tools, providing they are installed. Installed software required include Bowtie (I used v1.1.2) and SAMtools (v1.2) to give outputs of bam files. Also required is Perl and of course Trinity – if running within the streamlined utilities.

#PBS -P project name
#PBS -N AP0_alignment
#PBS -l nodes=1:ppn=20
#PBS -l walltime=20:00:00
#PBS -l pmem=24gb
#PBS -e ./AP0_alignment.txt
#PBS -M [email protected]
#PBS -m abe

# Load modules
module load perl
module load bowtie
module load java
module load samtools/1.2
module load trinity/2.1.1

# Working directory
cd /path to working directory

# Run bowtie script
/usr/local/trinity/2.1.1/util/ –seqType fq
–left AP0_R1_pairedwithunpaired.trim.fq –right AP0_R2_pairedwithunpaired.trim.fq
–target Syzygium.fasta –aligner bowtie
— -p 4 –all –best –strata -m 300

From this will be output a folder called bowtie containing bam files and indexed bams. For the next step you will use the bam files from all samples and all times to determine the counts per transcript.I rename the bam files according the plant/time eg. AP0_coordSorted.bam and AP0_coordSorted.bam.bai

Version 2.4.2 - Oct 5, 2020

  • Fixed an issue that would cause the bowtie2 wrapper script to throw an error when using wrapper-specific arguments.
  • Added new --sam-append-comment flag that appends comment from FASTA/Q read to corresponding SAM record.
  • Fixed an issue that would cause qupto , -u , to overflow when there are >= 2 32 query sequences (PR #312).
  • Fixed an issue that would cause bowtie2-build script to incorrectly process reference files.

Trinity: Error cannot find path to bowtie2 #452

The text was updated successfully, but these errors were encountered:

Kubu4 commented Oct 24, 2018

I'm not exactly sure what you've pasted here.

You shouldn't have all of these:

Kubu4 commented Oct 24, 2018

Where is your script located? I can't find it.

Also, I needed this line when I last ran Trinity (doesn't resolve bowtie error, but will prevent a problem with Trinity later on)

Kubu4 commented Oct 24, 2018

bowtie2 error is because bowtie2 isn't currently in your $PATH . That means you'll probably also have to add jellyfish and salmon to your $PATH .

To do so, add the following text to your

Grace-ac commented Oct 24, 2018

Yeah, I'm not entirely sure what I'm doing!

I literally just copy and pasted the script from TextWrangler into my terminal while logged in to Mox.

. Which I now understand is not the way to go. Now I'm thinking that I send the job via the script file rather than copy and pasting it .

Kubu4 commented Oct 24, 2018

Correct, you cannot copy and paste script to run it. Script has to be on Mox and then needs a special command to run it see the Mox wiki.

Kubu4 commented Oct 24, 2018

I looked at script. You need to put the Custom PATH stuff in your

Kubu4 commented Oct 24, 2018

Also, after adding that to your

./bashrc file, you'll need to source the file so the computer finds the new info:

/.bashrc file. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub, or mute the thread.

Grace-ac commented Oct 24, 2018

/.bashrc file the same thing as the script file?

/.bashrc file the same thing as the script file? — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or mute the thread.

/.bashrc file the same thing as the script file? — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or mute the thread.

Kubu4 commented Oct 25, 2018

Those programs need to be available in the system $PATH for Trinity to run. Absolute or relative paths have no bearing, as long as the programs are available in the system $PATH.

Kubu4 commented Oct 25, 2018

Or, maybe that export PATH command will work in your SBATCH script the way you already have it. Hmmm.

Grace-ac commented Oct 25, 2018

well I guess we'll find out! just started running job on Mox

Kubu4 commented Oct 25, 2018

However, I noticed you'll be using this version of Trinity:

That's a pretty old version (2.40) and is nearly two years old at this point. I'd recommend using the newer version:

However, since you'll be using the old version, I don't think you'll actually need those programs in your PATH I think they came bundled with Trinity back then.

Grace-ac commented Oct 28, 2018

Will fix Trinity version in script and re do.

Any other things I should fix before I send the job again?

Grace-ac commented Oct 28, 2018

Oh, I didn't fully read the error correctly. thought it said it couldn't run trinity because it wasn't the updated version.

I'll check the fq files and compare to source!

Grace-ac commented Oct 30, 2018

These are the files I had in the script:

These are the files that are in the trinity_out_dir :

The numbers are correct in the .fastq file names, but I'm not sure if the added extensions of ".PwU.qtrim.fq" means that there was a problem?

Kubu4 commented Oct 30, 2018

but there is likely problem with your fq files

Why do you think this? I'm not following.

Grace-ac commented Oct 30, 2018

the files are from /nightingales/C_bairdi/. I downloaded them, and then uploaded them to my data directory on mox.

Kubu4 commented Oct 30, 2018

MD5 is a program that generates a unique code (checksum) for a file. Transferring data from one place to another can corrupt a file (larger files are more prone to corruption during transfer). You can use the MD5 checksum originally generated for the file to compare the MD5 checksum generated after a file is transferred. If the checksums match it means the transferred file is exactly the same as the original. If the checksums do not match, something got corrupted during transfer.

So, any time you copy/move any FastQ files, you should compare the checksums.

Pro tip: Using rsync to copy files actually has this functionality built in and will do it automatically.

Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 GB. Bowtie 2 supports gapped, local, and paired-end alignment modes.

Bowtie 2 is available from various package managers, notably Bioconda. With Bioconda installed, you should be able to install Bowtie 2 with conda install bowtie2 .

Containerized versions of Bowtie 2 are also available via the Biocontainers project (e.g. via Docker Hub).

You can also download Bowtie 2 sources and binaries from the "releases" tab on this page. Binaries are available for the Linux, Mac OS X, and Windows. By utilizing the SIMDE project Bowtie 2 now supports the following architectures: ARM64, PPC64, and s390x. If you plan to compile Bowtie 2 yourself, make sure you at least have the zlib library and header files installed. See the Building from source section of the manual for details.

Looking to try out Bowtie 2? Check out the Bowtie 2 UI (currently in beta).

bowtie2 takes a Bowtie 2 index and a set of sequencing read files and outputs a set of alignments in SAM format.

"Alignment" is the process by which we discover how and where the read sequences are similar to the reference sequence. An "alignment" is a result from this process, specifically: an alignment is a way of "lining up" some or all of the characters in the read with some characters from the reference in a way that reveals how they're similar. For example:

Where dash symbols represent gaps and vertical bars show where aligned characters match.

We use alignment to make an educated guess as to where a read originated with respect to the reference genome. It's not always possible to determine this with certainty. For instance, if the reference genome contains several long stretches of As ( AAAAAAAAA etc.) and the read sequence is a short stretch of As ( AAAAAAA ), we cannot know for certain exactly where in the sea of As the read originated.

bowtie2-build builds a Bowtie index from a set of DNA sequences. bowtie2-build outputs a set of 6 files with suffixes .1.bt2 , .2.bt2 , .3.bt2 , .4.bt2 , .rev.1.bt2 , and .rev.2.bt2 . In the case of a large index these suffixes will have a bt2l termination. These files together constitute the index: they are all that is needed to align reads to that reference. The original sequence FASTA files are no longer used by Bowtie 2 once the index is built.

Bowtie 2's .bt2 index format is different from Bowtie 1's .ebwt format, and they are not compatible with each other.

bowtie2-inspect extracts information from a Bowtie 2 index about what kind of index it is and what reference sequences were used to build it. When run without any options, the tool will output a FASTA file containing the sequences of the original references (with all non-A/C/G/T characters converted to Ns). It can also be used to extract just the reference sequence names using the -n/--names option or a more verbose summary using the -s/--summary option.

Now let us align our reads using bowtie

(Note: For simplicity we are going to put all of the bowtie related files into the same directory. For your own work, you may want to organize your file structure better than we have).

Let’s get bowtie from Sourceforge:

unzip the file, and create a directory for bowtie. In this case, the program is precompiled so it comes as a binary executable:

Copy the bowtie files to a directory in you shell search path, and then move back to the parent directory (/data/drosophila):

Let’s create a new directory, “drosophila_bowtie” where we are going to place all the bowtie results:

Now we are going to build an index of the Drosophila genome using bowtie just like we did with bwa. The original Drosophila reference genome is in the same location as we used before. Again, we have already performed the indexing step (it takes about 7 minutes), so if you want to try it yourself, index a copy so you don’t over-write the one we’ve pre-run for you:

Now we get to map! We are going to use the default options for bowtie for the moment. Let’s go through this. there are a couple of flags that we have set, since We have paired end reads for these samples, and multiple processors. The general format for bowtie is (don’t run this):

However we have some more details we want to include, so there are a couple of flags that we have to set. -S means that we want the output in SAM format. -p 2 is for multithreading (using more than one processor). In this case we have two to use. -1 -2 tells bowtie that these are paired end reads (the .fastq), and specifies which one is which.

This should take 35-40 minutes to run on the full dataset so we’ll run it on a trimmed version (should take about 3 minutes later we’ll give you pre-computed results for the full set.):

You may see warning messages like:

We will talk about some options you can set to deal with this.

Some additional useful arguments/options (at least for me) -m # Suppresses all alignments for a particular read if more than m reportable alignments exist. -v # no more than v mismatches in the entire length of the read -n -l # max number of mismatches in the high quality “seed”, which is the the first l base pairs of a read. -chunkmbs # number of mb of memory a thread is given to store path. Useful when you get warnings like above –best # make Bowtie “guarantee” that reported singleton alignments are “best” given the options –tryhard # try hard to find valid alignments, when they exit. VERY SLOW.


Improvements 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 [4–6]. 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 re-sequencing 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 algorithm developed by Baeza-Yaetes and Perleberg [13]. Spaced seeds have been shown to yield higher sensitivity than contiguous seeds of the same length [14, 15]. SHRiMP employs a combination of spaced seeds and the Smith-Waterman [16] algorithm to align reads with high sensitivity at the expense of speed. Eland is a commercial alignment program available from Illumina that uses a hash-based algorithm to align reads.

Bowtie uses a different and novel indexing strategy to create an ultrafast, memory-efficient short read aligner geared toward mammalian re-sequencing. In our experiments using reads from the 1,000 Genomes project, Bowtie aligns 35-base pair (bp) reads at a rate of more than 25 million reads per CPU-hour, which is more than 35 times faster than Maq and 300 times faster than SOAP under the same conditions (see Tables 1 and 2). Bowtie employs a Burrows-Wheeler index based on the full-text minute-space (FM) index, which has a memory footprint of only about 1.3 gigabytes (GB) for the human genome. The small footprint allows Bowtie to run on a typical desktop computer with 2 GB of RAM. The index is small enough to be distributed over the internet and to be stored on disk and re-used. Multiple processor cores can be used simultaneously to achieve even greater alignment speed. We have used Bowtie to align 14.3× coverage worth of human Illumina reads from the 1,000 Genomes project in about 14 hours on a single desktop computer with four processor cores.

Bowtie makes a number of compromises to achieve this speed, but these trade-offs are reasonable within the context of mammalian re-sequencing projects. If one or more exact matches exist for a read, then Bowtie is guaranteed to report one, but if the best match is an inexact one then Bowtie is not guaranteed in all cases to find the highest quality alignment. With its highest performance settings, Bowtie may fail to align a small number of reads with valid alignments, if those reads have multiple mismatches. If the stronger guarantees are desired, Bowtie supports options that increase accuracy at the cost of some performance. For instance, the '--best' option will guarantee that all alignments reported are best in terms of minimizing mismatches in the seed portion of the read, although this option incurs additional computational cost.

With its default options, Bowtie's sensitivity measured in terms of reads aligned is equal to SOAP's and somewhat less than Maq's. Command line options allow the user to increase sensitivity at the cost of greater running time, and to enable Bowtie to report multiple hits for a read. Bowtie can align reads as short as four bases and as long as 1,024 bases. The input to a single run of Bowtie may comprise a mixture of reads with different lengths.

The original FASTP program was designed for protein sequence similarity searching. Because of the exponentially expanding genetic information and the limited speed and memory of computers in the 1980s heuristic methods were introduced aligning a query sequence to entire data-bases. FASTA, published in 1987, added the ability to do DNA:DNA searches, translated protein:DNA searches, and also provided a more sophisticated shuffling program for evaluating statistical significance. [2] There are several programs in this package that allow the alignment of protein sequences and DNA sequences. Nowadays, increased computer performance makes it possible to perform searches for local alignment detection in a database using the Smith–Waterman algorithm.

FASTA is pronounced "fast A", and stands for "FAST-All", because it works with any alphabet, an extension of the original "FAST-P" (protein) and "FAST-N" (nucleotide) alignment tools.

The current FASTA package contains programs for protein:protein, DNA:DNA, protein:translated DNA (with frameshifts), and ordered or unordered peptide searches. Recent versions of the FASTA package include special translated search algorithms that correctly handle frameshift errors (which six-frame-translated searches do not handle very well) when comparing nucleotide to protein sequence data.

In addition to rapid heuristic search methods, the FASTA package provides SSEARCH, an implementation of the optimal Smith–Waterman algorithm.

A major focus of the package is the calculation of accurate similarity statistics, so that biologists can judge whether an alignment is likely to have occurred by chance, or whether it can be used to infer homology. The FASTA package is available from the University of Virginia [3] and the European Bioinformatics Institute. [4]

The FASTA file format used as input for this software is now largely used by other sequence database search tools (such as BLAST) and sequence alignment programs (Clustal, T-Coffee, etc.).

FASTA takes a given nucleotide or amino acid sequence and searches a corresponding sequence database by using local sequence alignment to find matches of similar database sequences.

The FASTA program follows a largely heuristic method which contributes to the high speed of its execution. It initially observes the pattern of word hits, word-to-word matches of a given length, and marks potential matches before performing a more time-consuming optimized search using a Smith–Waterman type of algorithm.

The size taken for a word, given by the parameter kmer, controls the sensitivity and speed of the program. Increasing the k-mer value decreases number of background hits that are found. From the word hits that are returned the program looks for segments that contain a cluster of nearby hits. It then investigates these segments for a possible match.

There are some differences between fastn and fastp relating to the type of sequences used but both use four steps and calculate three scores to describe and format the sequence similarity results. These are:

  • Identify regions of highest density in each sequence comparison. Taking a k-mer to equal 1 or 2.
  • Rescan the regions taken using the scoring matrices. trimming the ends of the region to include only those contributing to the highest score.
  • In an alignment if several initial regions with scores greater than a CUTOFF value are found, check whether the trimmed initial regions can be joined to form an approximate alignment with gaps. Calculate a similarity score that is the sum of the joined regions penalising for each gap 20 points. This initial similarity score (initn) is used to rank the library sequences. The score of the single best initial region found in step 2 is reported (init1).
  • Use a banded Smith–Waterman algorithm to calculate an optimal score for alignment.

FASTA cannot remove low complexity regions before aligning the sequences as it is possible with BLAST. This might be problematic as when the query sequence contains such regions, e.g. mini- or microsatellites repeating the same short sequence frequent times, this increases the score of not familiar sequences in the database which only match in this repeats, which occur quite frequently. Therefore, the program PRSS is added in the FASTA distribution package. PRSS shuffles the matching sequences in the database either on the one-letter level or it shuffles short segments which length the user can determine. The shuffled sequences are now aligned again and if the score is still higher than expected this is caused by the low complexity regions being mixed up still mapping to the query. By the amount of the score the shuffled sequences still attain PRSS now can predict the significance of the score of the original sequences. The higher the score of the shuffled sequences the less significant the matches found between original database and query sequence. [5]

The FASTA programs find regions of local or global similarity between Protein or DNA sequences, either by searching Protein or DNA databases, or by identifying local duplications within a sequence. Other programs provide information on the statistical significance of an alignment. Like BLAST, FASTA can be used to infer functional and evolutionary relationships between sequences as well as help identify members of gene families.

Bowtie: cannot read fasta file? - Biology

The analytical steps are preset, and given by pipeline-managers. Please mail to [email protected] if you have any questions.

1. Reference Genome Mapping

1-1. Analytical steps of respective analytic tools

Maq performs analysis by every 200M reads。Thus we split a query file to multiple files.

In the case of single-end analysis:

Split 200M reads with Detail view window.
read : RUN Accession_0000
RUN Accession_0001

maq fasta2bfa in.ref.fasta out.ref.bfa Maq Prepare to make 'alignment'.
Convert file format of the reference FASTA to bfa.
maq fastq2bfq(fasta2bfa) in.read1.fastq(.fasta) out.read1.bfq(.bfa) Maq Prepare to make 'alignment'.
Convert file format of the reads FASTA to bfq.
maq map [option] in.ref.bfa in.read1.bfq(.bfa) Maq Align reads to the reference sequences.
maq mapmerge Maq Marge the result to make query file split and align.
maq mapview mapview.txt Maq Convert file format of the result binary to text.
The alignment result was included in 'mapview.txt'.
maq mapcheck in.ref.bfa > mapcheck.txt Maq Check the qualities of the reads.
The result was included in 'mapcheck.txt'.
maq indelsoa in.ref.bfa > out.indel.soa Maq Detection of indels and break points.
The result was included in 'out.indel.soa' file.
maq assemble [option] out.cns in.ref.bfa Maq Generation of the consensus sequences of the alignments.
The result was included in 'out.cns' file.
maq cns2snp out.cns > out.snp Maq Detection of SNPs.
The result was included in 'out.snp' file. SNPfilter [ option ] out.snp > out.filter.snp Maq SNP filter.
maq2sam > out.sam SAMtools
Convert the format of the alignment Maq to SAM.
SAM formatted result in SAM was included in 'out.sam'.

In the case of Paired-end analysis:

Split 200M reads each, processed que were appeared in 'Detail view' of the pipeline such as bellow.

read1 : RUN Accession_1_0000
RUN Accession_1_0001

read2 : RUN Accession_2_0000
RUN Accession_2_0001

ex.) read1 : DRR000001_1_0000
read2 : DRR000001_2_0000

Watch the video: Make your own FASTA file (October 2022).