Whether you want to analyze only one compartment, or integrate all three with diverse datatypes, we are experts in analyzing each data type with the highest standards and will use dataset specific integration statistics so that using more data, leads to more conclusions and a more focused picture.
Analyzing whole genome sequencing data is a computational intensive process, but the no other 'omics data can provide relational information or the origin / susceptibility of specific diseases, especially infectious diseases.
High-quality reference genome assemblies by de novo assembly is done with the use of open source tools like PICARD, GATK, BWA and Samtools and accurately annotating genes in these assemblies is done with species specific tools.
We can compare sequence reads with a reference for variant calling of SNP's, insertions, deletions, inversions, and structural rearrangements of any size.
Genome-level comparative analysis, such as synteny and identification of horizontal gene transfers and orthologs can be done.
RNA-Seq provides the ability to look at changes in gene expression in a binary or time course experimental design. We use various open source tools, primarily those that support R/Bioconductor to determine relative and absolute quantitative changes in experimental samples. Along with our network construction capabilities, we can discover co-expressed gene clusters or networks of genes that may include features of interest.
Additional information we can gather from RNA-seq data include; alternative splicing events, transcript fusions, small nucleotide variation discovery, and transcribed mutations along with differential expression analyses.
We can assemble transcripts from reference genomes or assemble de novo to determine novel exon/intron boundaries or amend previously annotated 5' and 3' gene boundaries.
Currently, we are using the following tools and analyses pipelines; BowTie, TopHat, STAR, Trinity, Exonerate, Transdecoder, CuffLinks, DESeq, and EdgeR.
Global proteomics has met the promises made decades ago. It is now possible to identify and quantify thousands of proteins in one experiment. Plus, detection of various post-translational modifications like phosphorylation, acetylation and glycosylation is now routine. We use open source tools like TransProteomics Pipeline, with several different search algorithms, OMSSA, MassWiz, Lutefisk, and X!Tandem.
Differential expression analysis on the peptide or the protein can be done with ABACUS, Xpress, and/or QProt.