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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://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/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

https://github.com/broadinstitute/Drop-seq

Software Java tools for analyzing Drop-seq data. Used to analyze gene expression from thousands of individual cells simultaneously. Analyzes mRNA transcripts while remembering origin cell transcript.

Proper citation: Drop-seq tools (RRID:SCR_018142) Copy   

•   Source: SciCrunch Registry

https://github.com/sreeramkannan/Shannon

Software tool for de novo transcriptome assembly from RNA-Seq data.

Proper citation: Shannon (RRID:SCR_017037) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/SNPRelate/

Software R package as parallel computing toolset for relatedness and principal component analysis of SNP data.

Proper citation: SNPRelate (RRID:SCR_022719) Copy   

•   Source: SciCrunch Registry

https://github.com/xfengnefx/hifiasm-meta

Software tool as metagenome assembler that exploits high accuracy of recent data. De novo metagenome assembler, based on haplotype resolved de novo assembler for PacBio Hifi reads. Workflow consists of optional read selection, sequencing error correction, read overlapping, string graph construction and graph cleaning.

Proper citation: hifiasm-meta (RRID:SCR_022771) Copy   

•   Source: SciCrunch Registry

https://CRAN.R-project.org/package=ComplexUpset

Software R package for visualization of intersecting sets. Used for quantitative analysis of sets, their intersections, and aggregates of intersections. Visualizes set intersections in matrix layout and introduces aggregates based on groupings and queries.

Proper citation: ComplexUpset (RRID:SCR_022752) Copy   

•   Source: SciCrunch Registry

https://github.com/FunctionLab/sei-framework

Web server for systematically predicting sequence regulatory activities and applying sequence information to human genetics data. Provides global map from any sequence to regulatory activities, as represented by sequence classes, and each sequence class integrates predictions for chromatin profiles like transcription factor, histone marks, and chromatin accessibility profiles across wide range of cell types.

Proper citation: sei (RRID:SCR_022571) Copy   

•   Source: SciCrunch Registry

https://upset.app/#:~:text=UpSet%20plots%20the%20intersections%20of,is%20part%20of%20an%20intersection.

Software tool to visualize set intersections in matrix layout. Interactive, web based visualization technique designed to analyze set based data. Visualizes both, set intersections and their properties, and elements in dataset. Used for quantitative analysis of data with more than three sets.

Proper citation: UpSet (RRID:SCR_022731) Copy   

•   Source: SciCrunch Registry

https://github.com/walaj/svaba

Software tool for detecting structural variants in sequencing data using genome wide local assembly. Genome wide detection of structural variants and indels by local assembly. Used for detecting SVs from short read sequencing data using genome wide local assembly with low memory and computing requirements.

Proper citation: SvABA (RRID:SCR_022998) 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://github.com/tobiasrausch/alfred

Web application as interactive multi-sample BAM alignment statistics, feature counting and feature annotation for long- and short-read sequencingas.

Proper citation: Alfred (RRID:SCR_023354) 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://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://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

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://www.sanger.ac.uk/science/tools/seqtools

Software for multiple sequence alignment viewing, editing and phylogeny. It includes a set of user-configurable modes to color residues used to create high-quality reference alignments.

Proper citation: Belvu (RRID:SCR_015989) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/science/tools/seqtools

Software for sequence alignment that is a graphical dot-matrix program for detailed comparison of two sequences.

Proper citation: Dotter (RRID:SCR_016080) Copy   

•   Source: SciCrunch Registry