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yf0205's Projects

arriba icon arriba

Fast and accurate gene fusion detection from RNA-Seq data

awesome-pipeline icon awesome-pipeline

A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin

bamsurgeon icon bamsurgeon

tools for adding mutations to existing .bam files, used for testing mutation callers

bioinformatics icon bioinformatics

:microscope: Path to a free self-taught education in Bioinformatics!

bioinformaticsprojectunisa2017 icon bioinformaticsprojectunisa2017

A project developed for the bioinformatics course at the University of Salerno 2016/2017. The goal of the project was to develop a classifier, based on pathways, to identify subclass of patients affected by tumors. The proposed methodology is divided into four steps: (i) Dimensionality reduction: since the gene expression data is high dimensional the DFP algorithm was used to identify the discriminant genes (ii) Pathways identification: the enrichment analysis is used to identify those biological pathways (KEGG or Reactome) that are statistically represented by the genes identified in the previous step (iii) Classification of the patients based on SVM: for each pathway a linear SVM is trained in cross validation. The most relevant genes are also identified. (iv) Graphical representation: a graph is construct to represent the pathways relationships The code was developed in R and Python.

blogdown icon blogdown

Create Blogs and Websites with R Markdown

bwa icon bwa

Burrow-Wheeler Aligner for short-read alignment (see minimap2 for long-read alignment)

circexplorer icon circexplorer

A combined strategy to identify circular RNAs (circRNAs and ciRNAs) (Zhang et al., Complementary Sequence-Mediated Exon Circularization, Cell (2014), 159:134-147)

codex-nf icon codex-nf

Pipeline for copy number variant calling from Whole Exome sequencing data using CODEX

complexheatmap icon complexheatmap

make complex heatmaps as well as self define annotation graphics

deepvariant icon deepvariant

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

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