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Orthology is the cornerstone of comparative genomics and gene function prediction. OrthoDB aims to classify protein-coding genes from the increasing number of available sequenced genomes into groups of orthologs descended from a single gene of the last common ancestor (LCA) of each clade of species. Applying this concept to the hierarchy of LCAs along the species phylogeny results in multiple levels of orthology: the more closely-related the species, the more finely-resolved the orthologous relations.

Samtools is a suite of programs for interacting with high-throughput sequencing data. It consists of three separate repositories:

• Samtools – Reading/writing/editing/indexing/viewing SAM/BAM/CRAM format
• BCFtools – Reading/writing BCF2/VCF/gVCF files and calling/filtering/summarising SNP and short indel sequence variants
• HTSlib – A C library for reading/writing high-throughput sequencing data

Samtools and BCFtools both use HTSlib internally, but these source packages contain their own copies of HSTlib so they can be built independently.

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License: MIT/Expat License

Application: Bioinformatics

Platform: Linux-64

Citation: Li H., Handsaker B., Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R. and 1000 Genome Project Data Processing Subgroup (2009) The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics, 25, 2078-9. [PMID: 19505943]

* Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011 Nov 1;27(21):2987-93. Epub 2011 Sep 8. [PMID: 21903627]

* Danecek P., Schiffels S., Durbin R. Multiallelic calling model in bcftools (-m), samtools.github.io/bcftools/call-m.pdf

* Li H. Improving SNP discovery by base alignment quality. Bioinformatics. 2011 Apr 15;27(8):1157-8. doi: 10.1093/bioinformatics/btr076. Epub 2011 Feb 13. [PMID: 21320865]

* Durbin R. Segregation based metric for variant call QC, samtools.github.io/bcftools/rd-SegBias.pdf

* Li H, Mathematical Notes on SAMtools Algorithms, www.broadinstitute.org/gatk/media/docs/Samtools.pdf

GAMESS is a program for ab initio molecular quantum chemistry. Briefly, GAMESS can compute SCF wavefunctions ranging from RHF, ROHF, UHF, GVB, and MCSCF. Correlation corrections to these SCF wavefunctions include Configuration Interaction, second order perturbation Theory, and Coupled-Cluster approaches, as well as the Density Functional Theory approximation. Excited states can be computed by CI, EOM, or TD-DFT procedures. Nuclear gradients are available, for automatic geometry optimization, transition state searches, or reaction path following.

Computation of the energy hessian permits prediction of vibrational frequencies, with IR or Raman intensities. Solvent effects may be modeled by the discrete Effective Fragment potentials, or continuum models such as the Polarizable Continuum Model. Numerous relativistic computations are available, including infinite order two component scalar relativity corrections, with various spin-orbit coupling options. The Fragment Molecular Orbital method permits use of many of these sophisticated treatments to be used on very large systems, by dividing the computation into small fragments. Nuclear wavefunctions can also be computed, in VSCF, or with explicit treatment of nuclear orbitals by the NEO code.

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License: User License Agreement from ISUQCG (http://www.msg.chem.iastate.edu/)

Application: Quantum chemistry, Molecular mechanics

Platform: Linux-64

Citation: General Atomic and Molecular Electronic Structure System” M.W.Schmidt, K.K.Baldridge, J.A.Boatz, S.T.Elbert, M.S.Gordon, J.H.Jensen, S.Koseki, N.Matsunaga, K.A.Nguyen, S.Su, T.L.Windus, M.Dupuis, J.A.Montgomery J. Comput. Chem., 14, 1347-1363(1993).

Advances in electronic structure theory: GAMESS a decade later” M.S.Gordon, M.W.Schmidt pp. 1167-1189, in “Theory and Applications of Computational Chemistry: the first forty years” C.E.Dykstra, G.Frenking, K.S.Kim, G.E.Scuseria (editors), Elsevier, Amsterdam, 2005.

The FASTX-Toolkit is a collection of command line tools for Short-Reads FASTA/FASTQ files preprocessing.  Next-Generation sequencing machines usually produce FASTA or FASTQ files, containing multiple short-reads sequences (possibly with quality information).  The main processing of such FASTA/FASTQ files is mapping (aka aligning) the sequences to reference genomes or other databases using specialized programs. Example of such mapping programs are:

• Blat
• SHRiMP
• LastZ
• MAQ and many many others

However, It is sometimes more productive to preprocess the FASTA/FASTQ files before mapping the sequences to the genome – manipulating the sequences to produce better mapping results. The FASTX-Toolkit tools perform some of these preprocessing tasks.

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License: Affero GPL (AGPL) version 3

Application: Bioinformatics

Platform: Linux-64

Citation: n/a

FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis.

The main functions of FastQC are:

• Import of data from BAM, SAM or FastQ files (any variant)
• Providing a quick overview to tell you in which areas there may be problems
• Summary graphs and tables to quickly assess your data
• Export of results to an HTML based permanent report
• Offline operation to allow automated generation of reports without running the interactive application

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License: GPL v3

Application: Bioinformatics

Platform:

Citation: n/a

Cufflinks assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. It accepts aligned RNA-Seq reads and assembles the alignments into a parsimonious set of transcripts. Cufflinks then estimates the relative abundances of these transcripts based on how many reads support each one, taking into account biases in library preparation protocols.

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License: Boost Software License

Application: Bioinformatics

Platform: Linux-64

Citation: n/a

The CRYSTAL Team is pleased to announce the release of CRYSTAL14 (current version: v1.0.3). CRYSTAL14 is a major release and the most relevant new features are:

• Static first- and second-hyperpolarizability and the corresponding electric susceptibilities tensors through a Coupled Perturbed HF/KS scheme
• Improved phonon dispersion calculation (phonon band structure and DOSs, ADPs and Debye-Waller factors, …)
• Raman and IR intensities through a CPHF/KS approach
• Automated calculation of the piezoelectric and photoelastic tensors of crystalline systems
• New DFT functionals: mGGA, Range-separated hybrids and Double-hybrids
• Automatic generation of fullerene-like structures
• New tools to model low-dimensionality systems (nanorods, nanoparticles, …)
• New tools for the treatment of solid solutions
• Improved Massive-parallel version (MPPcrystal – distributed memory)
• Internal interface to CRYSCOR for electronic structure calculations of 1D,- 2D- and 3D-periodic non-conducting systems at the L-MP2 correlated level and Double-Hybrids
• Internal interface to TOPOND for topological analysis of the charge density

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License: University-Wide Commercial Site License

Application: Quantum chemistry, Molecular mechanics

Platform: Linux-64

Citation: R. Dovesi, V.R. Saunders, C. Roetti, R. Orlando, C. M. Zicovich-Wilson, F. Pascale, B. Civalleri, K. Doll, N.M. Harrison, I.J. Bush, Ph. D’Arco, M. Llunell, M. Causa, Y. NoelCRYSTAL14 User’s Manual, University of Torino, Torino, 2014

BWA is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome. It consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. The first algorithm is designed for Illumina sequence reads up to 100bp, while the rest two for longer sequences ranged from 70bp to 1Mbp. BWA-MEM and BWA-SW share similar features such as long-read support and split alignment, but BWA-MEM, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate. BWA-MEM also has better performance than BWA-backtrack for 70-100bp Illumina reads.

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License: GPL v3

Application: Bioinformatics

Platform: Linux-64

Citation: If you use the BWA-backtrack algorithm, please cite the following paper: Li H. and Durbin R. (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25, 1754-1760. [PMID: 19451168]

* If you use the BWA-SW algorithm, please cite: Li H. and Durbin R. (2010) Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics, 26, 589-595. [PMID: 20080505]

* If you use the fastmap component of BWA, please cite: Li H. (2012) Exploring single-sample SNP and INDEL calling with whole-genome de novo assembly. Bioinformatics, 28, 1838-1844. [PMID: 22569178] * Li H. (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-M

Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 GB. Bowtie 2 supports gapped, local, and paired-end alignment modes.

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Licence: Open Source

Application: Bioinformatics

Platform: Linux-64

Citation:

Langmead B., Salzberg S. Fast gapped-read alignment with Bowtie 2. Nature Methods. 2012, 9:357-359, www.nature.com/nmeth/journal/v9/n4/full/nmeth.1923.html.

Bowtie is an ultrafast, memory-efficient short read aligner. It aligns short DNA sequences (reads) to the human genome at a rate of over 25 million 35-bp reads per hour. Bowtie indexes the genome with a Burrows-Wheeler index to keep its memory footprint small: typically about 2.2 GB for the human genome (2.9 GB for paired-end).

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License: Open Source

Application: Bioinformatics

Platform: Linux-64

Citation:

Langmead B., Trapnell C., Pop M., Salzberg S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25, genomebiology.com/2009/10/3/r25.