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https://github.com/chhylp123/hifiasm

Software tool as haplotype resolved de novo assembler for PacBio Hifi reads. Can assemble human genome in several hours.Introduces new graph binning algorithm and achieves haplotype resolved assembly given trio data. Takes advantage of long high fidelity sequence reads to represent haplotype information in phased assembly graph. Preserves contiguity of all haplotypes.

Proper citation: Hifiasm (RRID:SCR_021069) Copy   

•   Source: SciCrunch Registry

http://owlsim.org

Software package that provides the ability to do a number of standard semantic similarity methods and includes novel methods for combining these with dynamic selection of anonymous grouping classes. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: OwlSim (RRID:SCR_006819) Copy   

•   Source: SciCrunch Registry

https://github.com/kstreet13/slingshot

Software R package for identifying and characterizing continuous developmental trajectories in single cell data. Cell lineage and pseudotime inference for single-cell transcriptomics.

Proper citation: Slingshot (RRID:SCR_017012) Copy   

•   Source: SciCrunch Registry

http://avis.princeton.edu/pixie/index.php

bioPIXIE is a general system for discovery of biological networks through integration of diverse genome-wide functional data. This novel system for biological data integration and visualization, allows you to discover interaction networks and pathways in which your gene(s) (e.g. BNI1, YFL039C) of interest participate. The system is based on a Bayesian algorithm for identification of biological networks based on integrated diverse genomic data. To start using bioPIXIE, enter your genes of interest into the search box. You can use ORF names or aliases. If you enter multiple genes, they can be separated by commas or returns. Press ''submit''. bioPIXIE uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the bioPIXIE algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. As you move the mouse over genes in the network, interactions involving these genes are highlighted. If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed. You may need to download the Adobe Scalable Vector Graphic (SVG) plugin to utilize the visualization tool (you will be prompted if you need it).

Proper citation: bioPIXIE (RRID:SCR_004182) Copy   

•   Source: SciCrunch Registry

https://reich.hms.harvard.edu/software

Software application that finds skews in ancestry that are potentially associated with disease genes in recently mixed populations like African Americans. It can be downloaded for either UNIX or Linux.

Proper citation: Ancestrymap (RRID:SCR_004353) Copy   

•   Source: SciCrunch Registry

http://chgr.mc.vanderbilt.edu/page/gist

Software package to test if a marker can account in part for the linkage signal in its region. There are two versions of the software: Windows and Linux/Unix.

Proper citation: Genotype-IBD Sharing Test (RRID:SCR_006257) Copy   

•   Source: SciCrunch Registry

https://github.com/hetio/hetmatpy

Software Python package for matrix storage and operations on hetnets. Enables identifying relevant network connections between set of query nodes.

Proper citation: HetMatPy (RRID:SCR_023409) Copy   

•   Source: SciCrunch Registry

https://upsetplot.readthedocs.io/en/stable/

Software Python implementation of UpSet plots to visualize set overlaps.

Proper citation: UpSetPlot (RRID:SCR_023225) Copy   

•   Source: SciCrunch Registry

https://cells.ucsc.edu/

Web based tool to visualize gene expression and metadata annotation distribution throughout single cell dataset or multiple datasets. Interactive viewer for single cell expression. You can click on and hover over cells to get meta information, search for genes to color on and click clusters to show cluster specific marker genes.

Proper citation: UCSC Cell Browser (RRID:SCR_023293) Copy   

•   Source: SciCrunch Registry

https://pathvisio.org/

Software visualization tool for biological pathways. Pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. Developed in Java and can be extended with plugins.

Proper citation: PathVisio (RRID:SCR_023789) Copy   

•   Source: SciCrunch Registry

http://genomespace.org

Interoperability framework which supports integrative genomics analysis via access to various bioinformatics tools. Rather than performing analyses itself, GenomeSpace acts as a hub for data from supported bioinformatics tools and reformats data and results when necessary.

Proper citation: GenomeSpace (RRID:SCR_014967) Copy   

•   Source: SciCrunch Registry

https://github.com/virajbdeshpande/AmpliconArchitect

Software package designed to call circular DNA from short read WGS data.Used to identify one or more connected genomic regions which have simultaneous copy number amplification and elucidates architecture of amplicon.Used to reconstruct structure of focally amplified regions using whole genome sequencing and validate it extensively on multiple simulated and real datasets, across wide range of coverage and copy numbers.

Proper citation: AmpliconArchitect (RRID:SCR_023150) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/oligo.html

Software R package to analyze oligonucleotide arrays at probe level. Supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). Used for annotation of Affymetrix Gene Array data.

Proper citation: Preprocessing tools for oligonucleotide arrays (RRID:SCR_023726) Copy   

•   Source: SciCrunch Registry

https://github.com/churchmanlab/genewalk

Software for individual genes functions determination that are relevant in particular biological context and experimental condition. Quantifies similarity between vector representations of gene and annotated GO terms through representation learning with random walks on condition specific gene regulatory network. Similarity significance is determined through comparison with node similarities from randomized networks.

Proper citation: GeneWalk (RRID:SCR_023787) Copy   

•   Source: SciCrunch Registry

https://github.com/shendurelab/LACHESIS

Software tool for chromosome scale scaffolding of de novo genome assemblies based on chromatin interactions.Method exploits signal of genomic proximity in Hi-C datasets for ultra long range scaffolding of de novo genome assemblies.

Proper citation: LACHESIS (RRID:SCR_017644) Copy   

•   Source: SciCrunch Registry

https://github.com/Kingsford-Group/kourami

Software graph guided assembly for novel human leukocyte antigen allele discovery. Graph guided assembly for HLA haplotypes covering typing exons using high coverage whole genome sequencing data.Implemented in Java and supported on Linux and Mac OS X.

Proper citation: Kourami (RRID:SCR_022280) Copy   

•   Source: SciCrunch Registry

http://www.t-profiler.org

One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. The T-profiler tool hosted on this website uses the t-test to score changes in the average activity of pre-defined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif, and location on the same chromosome, respectively. If desired, an iterative procedure can be used to select a single, optimal representative from sets of overlapping gene groups. A jack-knife procedure is used to make calculations more robust against outliers. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters. Currently, gene expression data from Saccharomyces cerevisiae and Candida albicans are supported. Users can submit their microarray data for analysis by clicking on one of the two organism-specific tabs above. Platform: Online tool

Proper citation: T-profiler (RRID:SCR_003452) Copy   

•   Source: SciCrunch Registry

http://interactome.baderlab.org/

Project portal for the Human Reference Protein Interactome Project, which aims generate a first reference map of the human protein-protein interactome network by identifying binary protein-protein interactions (PPIs). It achieves this by systematically interrogating all pairwise combinations of predicted human protein-coding genes using proteome-scale technologies.

Proper citation: Human Reference Protein Interactome Project (RRID:SCR_015670) Copy   

•   Source: SciCrunch Registry

https://github.com/davidaknowles/leafcutter/

Software tool for identifying and quantifying RNA splicing variation. Used to study sample and population variation in intron splicing. Identifies variable intron splicing events from short read RNA-seq data and finds alternative splicing events of high complexity. Used for detecting differential splicing between sample groups, and for mapping splicing quantitative trait loci (sQTLs).

Proper citation: LeafCutter (RRID:SCR_017639) Copy   

•   Source: SciCrunch Registry

https://github.com/hahnlab/CAFExp

Software tool for computational analysis of gene family evolution. Used for statistical analysis of evolution gene family sizes. Models evolution of gene family sizes over phylogeny.

Proper citation: Computational Analysis of gene Family Evolution (RRID:SCR_018924) Copy   

•   Source: SciCrunch Registry