Here are detailed tutorials that describe how to perform some NGS analysis tasks supported by Goby.
New in Goby 3.0. Use deep learning models to call somatic variations.
Discover genomic variants Find SNPs and mutations in samples, estimate allelelic frequencies, methylation rate, or and identify genomic positions where allele frequencies differ significantly between groups of samples.
What’s new in Goby 2.0 A brief introduction to the new features introduced in Goby 2.0 (June 2012).
Ultra-fast local realignment around indels Goby implements a greedy algorithm to realign sequence variations around indels. This tutorial describes how to perform local realignment as a pre-processing step (a la SRMA), or combined with a sequence variation analysis.
Displaying splice forms RNASeq data alignments written in Goby format can encode splicing information. This tutorial describes how to generate such alignments and view them in IGV.
DNA methylation analysis Analyze bisulfite treated reads to estimate methylation rates throughout the genome, view the result in the local genomic context with IGV.
discover genomic variants Find SNPs and mutations in samples, estimate allelelic frequencies, methylation rate, or and identify genomic positions where allele frequencies differ significantly between groups of samples.
convert SAM files to Goby format It is possible to convert SAM/BAM files to Goby format. This is useful if you have already done alignments and generated SAM/BAM files. This tutorial describes how to do this.
annotate reads with meta-data Add meta-data to a compact-reads file to track when the sample was sequenced, what platform was used for sequencing, or other meta-data about the reads.
filter redundant reads Remove repeated sequences from an input file or obtain read multiplicity information
differential expression analysis Find genes or exons differentially expressed across groups of samples.
display sequence variations Display sequence variations stored in an alignment.
create wiggle/bed tracks Create wiggle or BED tracks for visualization on a genome viewer/browser.
estimate heptamer weights Hansen KD et al have described a method to reweight base level position counts to remove systematic hexamer priming bias. This tutorial describes how to estimate these weights associated with individual reads and to correct counts with the weights before performing differential expression analyses.
Handling barcoded reads Decode barcodes embedded in read sequences.
Sort and index alignment files Sorting and indexing alignment files improves performance of analyses tools that need to process only subsets of genomic locations.
Developing with Goby An introduction to the basics of the Goby framework, for programmers intereted in building scalable next-gen analysis programs.