| sickle |
sickle se -t [solexa|illumina|sanger] -f input_reads.fastq -o output_reads_trimmed.fastq |
Sickle is a tool that uses sliding windows along with quality and length thresholds to determine when quality is sufficiently low to trim the 3'-end of reads and also determines when the quality is sufficiently high enough to trim the 5'-end of reads. It will also discard reads based upon the length threshold. It takes the quality values and slides a window across them whose length is 0.1 times the length of the read. If this length is less than 1, then the window is set to be equal to the length of the read. Otherwise, the window slides along the quality values until the average quality in the window rises above the threshold, at which point the algorithm determines where within the window the rise occurs and cuts the read and quality there for the 5'-end cut. Then when the average quality in the window drops below the threshold, the algorithm determines where in the window the drop occurs and cuts both the read and quality strings there for the 3'-end cut. However, if the length of the remaining sequence is less than the minimum length threshold, then the read is discarded entirely (or replaced with an "N" record). 5'-end trimming can be disabled. |
| multiBamSummary |
multiBamSummary bins --bamfiles file1.bam file2.bam -out results.npz |
This tool generates a matrix of read coverages for a list of genomic regions and at least two samples (BAM files). The genome is split into bins of the given size. For each bin, the number of reads found in each BAM file is counted. Alternatively, an interval file with pre-defined genomic regions can be provided. |
| multiBamSummary |
multiBamSummary BED-file --BED selection.bed --bamfiles file1.bam file2.bam -out results.npz |
This tool generates a matrix of read coverages for a list of genomic regions and at least two samples (BAM files). The genome is split into bins of the given size. For each bin, the number of reads found in each BAM file is counted. Alternatively, an interval file with pre-defined genomic regions can be provided. |
| bowtie-build |
bowtie-build [options]* <reference_in> <ebwt_base> |
bowtie-build builds a Bowtie index from a set of DNA sequences. bowtie-build outputs a set of 6 files with suffixes .1.ebwt, .2.ebwt, .3.ebwt, .4.ebwt, .rev.1.ebwt, and .rev.2.ebwt. (If the total length of all the input sequences is greater than about 4 billion, then the index files will end in ebwtl instead of ebwt.) These files together constitute the index: they are all that is needed to align reads to that reference. The original sequence files are no longer used by Bowtie once the index is built. |
| maq fastq2bfq |
maq fastq2bfq [-n nreads] in.read.fastq out.read.bfq|out.prefix |
Convert reads in FASTQ format to Maq’s BFQ (binary FASTQ) format. |
| maq bfq2fastq |
maq bfq2fastq in.read.bfq out.read.fastq |
Convert Maq’s BFQ format to standard FASTQ format. |
| Trimmomatic |
java -jar <path to trimmomatic jar> SE [-threads <threads>] [-phred33 | -phred64] [-trimlog <logFile>] <input> <output> <step 1> ... or java -classpath <path to trimmomatic jar> org.usadellab.trimmomatic.TrimmomaticSE [-threads <threads>] [-phred33 | -phred64] [-trimlog <logFile>] <input> <output> <step 1> ... |
Trimmomatic performs a variety of useful trimming tasks for illumina single ended data. |
| Trimmomatic |
java -jar <path to trimmomatic.jar> PE [-threads <threads] [-phred33 | -phred64] [-trimlog <logFile>] >] [-basein <inputBase> | <input 1> <input 2>] [-baseout <outputBase> | <unpaired output 1> <paired output 2> <unpaired output 2> <step 1> ... or java -classpath <path to trimmomatic jar> org.usadellab.trimmomatic.TrimmomaticPE [-threads <threads>] [-phred33 | -phred64] [-trimlog <logFile>] [-basein <inputBase> | <input 1> <input 2>] [-baseout <outputBase> | <paired output 1> <unpaired output 1> <paired output 2> <unpaired output 2> <step 1> ... |
Trimmomatic performs a variety of useful trimming tasks for illumina paired-end data. |
| correctGCBias |
correctGCBias -b file.bam --effectiveGenomeSize 2150570000 -g mm9.2bit --GCbiasFrequenciesFile freq.txt -o gc_corrected.bam [options] |
This tool corrects the GC-bias using the method proposed by [Benjamini & Speed (2012). Nucleic Acids Research, 40(10)]. It will remove reads from regions with too high coverage compared to the expected values (typically GC-rich regions) and will add reads to regions where too few reads are seen (typically AT-rich regions). The tool computeGCBias needs to be run first to generate the frequency table needed here. |
| samtools rmdup |
samtools rmdup [-sS] <input.srt.bam> <out.bam> |
Remove potential PCR duplicates: if multiple read pairs have identical external coordinates, only retain the pair with highest mapping quality. In the paired-end mode, this command ONLY works with FR orientation and requires ISIZE is correctly set. It does not work for unpaired reads |
| PRINSEQ |
prinseq-lite.pl [-fasta|-fastq] input_reads.[fasta|fastq] -out_format [1|2|3|4|5] [options] |
PRINSEQ is a tool that generates summary statistics of sequence and quality data and that is used to filter, reformat and trim next-generation sequence data. It is particular designed for 454/Roche data, but can also be used for other types of sequence data. PRINSEQ is available through a user-friendly web interface or as standalone version. The standalone version is primarily designed for data preprocessing and does not generate summary statistics in graphical form. |
| maq fasta2bfa |
maq fasta2bfa in.ref.fasta out.ref.bfa |
Convert sequences in FASTA format to Maq’s BFA (binary FASTA) format. |
| insertion_profile.py |
insertion_profile.py -s "PE" -i test.bam -o out
|
Calculate the distributions of inserted nucleotides across reads. |
| cd-hit-dup |
cd-hit-dup -i input.fa -o output.fa [other options] |
cd-hit-dup is a simple tool for removing duplicates from sequencing reads, with optional step to detect and remove chimeric reads. |
| sickle |
sickle pe -t [solexa|illumina|sanger] -f forward_reads.fastq -r reverse_reads.fastq -o trimmed_output_file1.fastq -p trimmed_output_file2.fastq -s trimmed_singles_file.fastq |
Sickle is a tool that uses sliding windows along with quality and length thresholds to determine when quality is sufficiently low to trim the 3'-end of reads and also determines when the quality is sufficiently high enough to trim the 5'-end of reads. It will also discard reads based upon the length threshold. It takes the quality values and slides a window across them whose length is 0.1 times the length of the read. If this length is less than 1, then the window is set to be equal to the length of the read. Otherwise, the window slides along the quality values until the average quality in the window rises above the threshold, at which point the algorithm determines where within the window the rise occurs and cuts the read and quality there for the 5'-end cut. Then when the average quality in the window drops below the threshold, the algorithm determines where in the window the drop occurs and cuts both the read and quality strings there for the 3'-end cut. However, if the length of the remaining sequence is less than the minimum length threshold, then the read is discarded entirely (or replaced with an "N" record). 5'-end trimming can be disabled. |
| maq sol2sanger |
maq sol2sanger in.sol.fastq out.sanger.fastq |
Convert Solexa FASTQ to standard/Sanger FASTQ format. |
| cd-hit-dup |
cd-hit-dup -i input.fa -o output |
cd-hit-dup is a simple tool for removing duplicates from sequencing reads, with optional step to detect and remove chimeric reads. A number of options are provided to tune how the duplicates are removed. |
| read_GC.py |
read_GC.py -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam -o output |
GC content distribution of reads. |
| bamPEFragmentSize |
bamPEFragmentSize [-h] [--bamfiles bam files [bam files ...]] [--histogram FILE] [--plotFileFormat FILETYPE] [--numberOfProcessors INT] [--samplesLabel SAMPLESLABEL [SAMPLESLABEL ...]] [--plotTitle PLOTTITLE] [--maxFragmentLength MAXFRAGMENTLENGTH] [--logScale] [--binSize INT] [--distanceBetweenBins INT] [--blackListFileName BED file] [--table FILE] [--outRawFragmentLengths FILE] [--verbose] [--version] |
This tool samples the given BAM files with paired-end data to estimate the fragment length distribution. Properly paired reads are preferred for computation, i.e., unless a region does not contain any concordant pairs, discordant pairs are ignored. |
| geneBody_coverage.py |
geneBody_coverage.py -r hg19.housekeeping.bed -i /data/alignment/ -o output |
Calculate the RNA-seq reads coverage over gene body. |