The Bioinformatics Core has written open-source bioinformatics software for the entire community to use and is freely available on our GitHub page. We have also created an AMI (Amazon Machine Image) that runs Galaxy loaded with standard as well as more customized software. You can use this AMI for command-line analysis as well. Our current public AMI ID is ami-f4733f94. See below for more detailed instructions on how to launch instances using our AMI.
A windowed adaptive trimming tool for FASTQ files using quality: download from GitHub
Most modern sequencing technologies produce reads that have deteriorating quality towards the 3′-end and some towards the 5′-end as well. Incorrectly called bases in both regions negatively impact assembles, mapping, and downstream bioinformatics analyses.
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.
A very simple adapter trimmer: download from GitHub
Scythe uses a Naive Bayesian approach to classify contaminant substrings in sequence reads. It considers quality information, which can make it robust in picking out 3′-end adapters, which often include poor quality bases.
The Bayesian approach Scythe uses compares two likelihood models: the probability of seeing the matches in a sequence given contamination, and not given contamination. Given that the read is contaminated, the probability of seeing a certain number of matches and mismatches is a function of the quality of the sequence. Given the read is not contaminated (and is thus assumed to be random sequence), the probability of seeing a certain number of matches and mismatches is chance. The posterior is calculated across both these likelihood models, and the class (contaminated or not contaminated) with the maximum posterior probability is the class selected.
Quick Read Quality Control: download from GitHub
qrqc is a fast and extensible R package that reports basic quality and summary statistics on FASTQ and FASTA files, including base and quality distribution by position, sequence length distribution, and common sequences.
A barcode demultiplexing and trimming tool for FASTQ files: download from GitHub
Next-generation sequencing can currently produce hundreds of millions of reads per lane of sample and that number increases at a dizzying rate. Barcoding individual sequences for multiple lines or multiple species is a cost-efficient method to sequence and analyze a broad range of data.
Sabre is a tool that will demultiplex barcoded reads into separate files. It will work on both single-end and paired-end data in fastq format. It simply compares the provided barcodes with each read and separates the read into its appropriate barcode file, after stripping the barcode from the read (and also stripping the quality values of the barcode bases). If a read does not have a recognized barcode, then it is put into a separate “unknown” file. Sabre also has an option to allow mismatches of the barcodes.