Researchers at the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, have released clusterMaker 1.10 — the latest version of a Cytoscape plugin for creating and visualizing clusters of genetic data.
The release includes several new clustering algorithms, including k-medoid, and adds an iterative approach to finding the best k for k-medoid and k-means using silhouette. It also includes tools to create correlation networks from node attributes, which can be used to create expression correlation networks from expression data, for example. Furthermore, clusterMaker now exports a variety of CyCommands, which can be used from scripts or from other plugins.
Among other capabilities, CummeRbund includes several plotting methods that let users visualize RNA-seq data quality and global statistics such as FPKM distribution. It also includes routines for plotting expression levels for one or more genes, their isoforms, TSS groups, or CDS groups, as well as formalized R classes for data access and manipulation.
This week, Xennex launched GeneCards 3.07, a database of human genes that was developed by the Weizmann Institute of Science.
This release includes 68,445 entries, 32,088 of them with symbols approved by the Human Genome Organization gene nomenclature committee. It also includes several new features like orthologs from non-eukaryotic domains, a new interaction network preview image, and a direct listing for mouse knockouts.
GeneCards is free for academic not-for-profit institutions. All other users need to purchase commercial licenses from Xennex.
This week, Knome launched a new informatics service called KnomeBase to help users interpret their genomic data.
The company is offering tools and services to annotate, compare, and distill raw sequence data in large whole-genome studies. As part of the service, customers will also receive a genome discovery kit, KnomeGDK — a suite of tools, scripts, and libraries for querying and visualizing gene interaction networks across multiple genomes.
BGI said this week that it has updated several of its next-generation sequence data analysis pipelines and software, including its assembly and binning tools, genetic variation software, and two cloud-based solutions for genomic-based research.
Specifically, the group said that the latest release of its Short Oligonucleotide Analysis Package toolkit includes SOAP3, a GPU-accelerated short read alignment tool; SOAPindel, an indel finder; SOAPfusion, a gene fusion detector; SOAPsplice, a splice-junction detector; SOAPdenovo-Trans, a de novo transcriptome assembler; and Metacluster 4.0, a binning solving tool for metagenomics data.
BGI also said that it has made improvements to Hecate 2, Gaea 2, GAMA, GSNP, and Adam, which are its cloud-based software offerings
Also this week, GigaScience, a research journal jointly published by BGI and BioMed Central, launched a free database dubbed GigaDB.
GigaDB hosts publicly available, large-scale datasets and provides a unique digital object identifier for each one so that researchers cite the resource when they use its data in independent publications.
This release of the database contains 17 new datasets in addition to existing information. This includes sequences from the E. coli O104 genome as well as data from other plants, animals, and microbes.
SmartGene has released several curated reference databases that contain clinically significant sequences from Nocardia, a species of gram-positive bacteria implicated in diseases like pneumonia and encephalitis.
The company plans to make the databases, which contain sequences of 16S rRNA and Sec A1 genes from Nocardia, available as an option for customers of its web-based bacterial sequence analysis module.
Copernicus offers molecular dynamics parallelized on three levels: SIMD, threads, and message-passing. These features are combined with kinetic clustering, statistical model building, and real-time result monitoring.
According to the developers, the system aims to take the scope of computer simulations from the level of individual simulation runs to one focused on obtaining results. It does this by allowing the user to specify these end results rather than providing a detailed prescription of how to obtain them. It is then up to the Copernicus run-time system to break these desired end results up into specific tasks and to run these tasks as efficiently as possible on the available computational resources.