vcf2tsv - Genomic VCF to tab-separated values

  •        70

Running vcf2tsv requires Python 3. It also requires that you have cyvcf2 and numpy installed. This can be achieved through the use of pip.



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  •    C++

The Variant Call Format (VCF) is a flat-file, tab-delimited textual format intended to concisely describe reference-indexed variations between individuals. VCF provides a common interchange format for the description of variation in individuals and populations of samples, and has become the defacto standard reporting format for a wide array of genomic variant detectors. The API itself provides a quick and extremely permissive method to read and write VCF files. Extensions and applications of the library provided in the included utilities (*.cpp) comprise the vast bulk of the library's utility for most users.

freebayes - Bayesian haplotype-based genetic polymorphism discovery and genotyping.

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FreeBayes is a Bayesian genetic variant detector designed to find small polymorphisms, specifically SNPs (single-nucleotide polymorphisms), indels (insertions and deletions), MNPs (multi-nucleotide polymorphisms), and complex events (composite insertion and substitution events) smaller than the length of a short-read sequencing alignment. FreeBayes uses short-read alignments (BAM files with Phred+33 encoded quality scores, now standard) for any number of individuals from a population and a reference genome (in FASTA format) to determine the most-likely combination of genotypes for the population at each position in the reference. It reports positions which it finds putatively polymorphic in variant call file (VCF) format. It can also use an input set of variants (VCF) as a source of prior information, and a copy number variant map (BED) to define non-uniform ploidy variation across the samples under analysis.

picard - A set of command line tools (in Java) for manipulating high-throughput sequencing (HTS) data and formats such as SAM/BAM/CRAM and VCF

  •    Java

A set of Java command line tools for manipulating high-throughput sequencing (HTS) data and formats. Picard is implemented using the HTSJDK Java library HTSJDK to support accessing file formats that are commonly used for high-throughput sequencing data such as SAM and VCF.

bioawk - BWK awk modified for biological data

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Bioawk is an extension to Brian Kernighan's awk, adding the support of several common biological data formats, including optionally gzip'ed BED, GFF, SAM, VCF, FASTA/Q and TAB-delimited formats with column names. It also adds a few built-in functions and an command line option to use TAB as the input/output delimiter. When the new functionality is not used, bioawk is intended to behave exactly the same as the original BWK awk. The original awk requires a YACC-compatible parser generator (e.g. Byacc or Bison). Bioawk further depends on zlib so as to work with gzip'd files.

gemini - a lightweight db framework for exploring genetic variation.

  •    Python

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The VCF Builder is an advanced development tool for creating C++ applications, and supporting a wide number of plugins for enhancing it's functionality. While the VCF Builder is capable of creating generic C++ applications, it's forte is building GUI ap

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  •    Python

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  •    Java

The variant-codec library provides a generic way to serialize objects. It uses a standardized structure, the variant, the encapsulate all data types.

Mach7 - Functional programming style pattern-matching library for C++

  •    C++

Pattern matching is an abstraction mechanism that can greatly simplify source code. Commonly, pattern matching is built into a language to provide better syntax, faster code, correctness guarantees and improved diagnostics. Mach7 is a library solution to pattern matching in C++ that maintains many of these features. All the patterns in Mach7 are user-definable, can be stored in variables, passed among functions, and allow the use of open class hierarchies. Next example demonstrates that the library can deal efficiently and in a type-safe manner with non-polymorphic classes like boost::variant as well.

Vc - SIMD Vector Classes for C++

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Recent generations of CPUs, and GPUs in particular, require data-parallel codes for full efficiency. Data parallelism requires that the same sequence of operations is applied to different input data. CPUs and GPUs can thus reduce the necessary hardware for instruction decoding and scheduling in favor of more arithmetic and logic units, which execute the same instructions synchronously. On CPU architectures this is implemented via SIMD registers and instructions. A single SIMD register can store N values and a single SIMD instruction can execute N operations on those values. On GPU architectures N threads run in perfect sync, fed by a single instruction decoder/scheduler. Each thread has local memory and a given index to calculate the offsets in memory for loads and stores. Current C++ compilers can do automatic transformation of scalar codes to SIMD instructions (auto-vectorization). However, the compiler must reconstruct an intrinsic property of the algorithm that was lost when the developer wrote a purely scalar implementation in C++. Consequently, C++ compilers cannot vectorize any given code to its most efficient data-parallel variant. Especially larger data-parallel loops, spanning over multiple functions or even translation units, will often not be transformed into efficient SIMD code.

OpenCC - A project for conversion between Traditional and Simplified Chinese

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VCF Viewer


Shows you all VCF files, that are inside chosen folder. You can view and read them, nothing more (at least now).


  •    Java

An open-source java accounting application that integrates OpenOffice, Apache Derby and HBCI/FinTS to create and manage invoices, credit memos, delivery notes, bills etc. Imports from kTimeTracker, Task Coach, VCF, Hibiscus, Moneyplex, Starmoney, exports to Winston, VCF, openTrans et. al.

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  •    Java

A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means. The t-digest algorithm is also very parallel friendly making it useful in map-reduce and parallel streaming applications. The t-digest construction algorithm uses a variant of 1-dimensional k-means clustering to produce a data structure that is related to the Q-digest. This t-digest data structure can be used to estimate quantiles or compute other rank statistics. The advantage of the t-digest over the Q-digest is that the t-digest can handle floating point values while the Q-digest is limited to integers. With small changes, the t-digest can handle any values from any ordered set that has something akin to a mean. The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by Q-digests in spite of the fact that t-digests are more compact when stored on disk.