Genome analysis

QC

Quality control for the raw reads was performed to access the sequence quality, GC content, the presence of adaptors, and the read length.

Please refer to the script: QC.sh

Mapping

We have used BWA, a software package for mapping low-divergent sequences against a large reference genome, such as the human genome. The BWA-MEM algorithm, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate.

Please refer to the script : BWA-MEM.sh

Variant detecting

We used freebayes, a Bayesian genetic variant detector designed to find small polymorphisms, specifically SNPs (single-nucleotide polymorphisms), indels (insertions and deletions), MNPs (multi-nucleotide polymorphisms), and complex events (composite insertion and substitution events) smaller than the length of a short-read sequencing alignment.

Please refer to the script : freebayes.sh

Genomic variant annotations and functional effect prediction

With the help of SnpEff we have performed the genetic variant annotation and functional effects. It also annotates and predicts the effects of genetic variants on genes and proteins (such as amino acid changes).

Please refer to the script : SnpEff.sh

gnomAD allele frequency

We developed a custom script that helps to grep the gnomAD allele frequency from the variant of interest.

Please refer to the script : gnomAD.sh

Rna seq analysis

Spliced Transcripts Alignment with STAR

To determine where on the genome our reads originated from, we aligned our reads to the reference genome using STAR (Spliced Transcripts Alignment to a Reference). STAR is an aligner designed to specifically address many of the challenges of RNA-seq data mapping using a strategy to account for spliced alignments

Please refer to the script : STAR_Index.sh & STAR.sh

Counting reads

HTSeq is a Python package that calculates the number of mapped reads to each gene.

Please refer to the script : HTSeq.sh

Transcript assembly and quantification

We have assembled the RNA-Seq alignments into potential transcripts and quantified them using StringTie.

Please refer to the scriptt : StringTie.sh

Differential expression Analysis

We have used the R package DESeq2 to analyze count data from high-throughput sequencing assays and test for differential expressions.

Please refer to the script : DESeq2.R and also refer to the folder DEG for step by step guidelines.

Gene ontology enrichment

Gene ontology enrichment analysis was performed with the R package GOfuncR (version 1.14.0) for up-and down-regulated DEGs. GO nodes with a family-wise error rate (FWER) <0.05 were considered significantly enriched.

Please refer to the Pipeline: https://rpubs.com/Akhil_Velluva/GOfuncR

Quantification of RNA splicing

The tool Leafcutter quantifies RNA splicing variation using short-read RNA-seq data. The core idea is to leverage spliced reads (reads that span an intron) to quantify (differential) intron usage across samples.

Please refer to the script : Leafcutter.sh

Somatic variants discovery

We have used Mutect2 to call somatic short mutations via local assembly of haplotypes. Short mutations include single nucleotide (SNA) and insertion and deletion (indel) alterations.

Please refer to the script : Mutect2.sh and also refer to the folder Variant_Calling for step by step guidelines.

Differentially expressed genes in lipedema study

Please refer to the file : Lipedema_Results.xlsx