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An efficient algorithm for clustering very large NGS sets. It joins sequences into clusters that can differ by up to three mismatches and three overhanging residues from their virtual center. It is based on a modified spaced seed method, called block spaced seeds. Its clustering component operates on the hash tables by first identifying virtual center sequences and then finding all their neighboring sequences that meet the similarity parameters. SEED can cluster 100 million short read sequences in <4 h with a linear time and memory performance.
ICC / Indel and Carryforward Correction
Analyzes pyrosequencing data for both library and amplicon applications. ICC mainly focuses on the correction of indel, carryforward and incomplete extension errors in sequencing with possible application on sequence from other technologies. The software supplies a four-steps pipeline including: (i) read quality filtering; (ii) BLAST and retrieval by sequence window; (iii) non-substitution clustering and error correction and; (iv) variant calling and profiling.
An exact algorithm to determine which pairs of sequences lie within a given Levenshtein distance. For error correction or redundancy reduction purposes, matched pairs are then merged into clusters of similar sequences. The efficiency of starcode is attributable to the poucet search, a novel implementation of the Needleman–Wunsch algorithm performed on the nodes of a trie. On the task of matching random barcodes, starcode outperforms sequence clustering algorithms in both speed and precision.
SHARAKU / SHape Aligner of non-coding RNA developed by Keio University
Aligns two read mapping profiles of next-generation sequencing outputs for non-coding RNAs. SHARAKU incorporates the primary and secondary sequence structures into an alignment of read mapping profiles to allow for the detection of common processing patterns. The tool exhibited superior performance to previous methods for correctly clustering the read mapping profiles with respect to 5’-end processing and 3’-end processing from degradation patterns and in detecting similar processing patterns in deriving the shorter RNAs. SHARAKU succeeded in identifying the significant clusters of read mapping profiles for similar processing patterns of small derived RNA families expressed in the brain.
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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.
A toolkit for processing and analysing RAD sequencing data. The tools are designed to process de novo RAD data, that is, data from species without a reference genome. RADtools integrates RADpools for separating raw Illumina reads into separate pools, RADtags for clustering the reads for each pool candidate RAD tags for that pool, RADmarkers for clustering tags across all pools into candidate loci with alleles, RADMIDS for designing a set of MIDS for use in RAD adapters. These RAD methods have great potential for creating genomic scaffolds to assist in genome assembly and for identifying thousands of sequence variants to aid in detection of major as well as minor quantitative traits.
GBS-SNP-CROP / GBS SNP Calling Reference Optional Pipeline
Discovers SNP and characterizes plant germplasm. GBS-SNP-CROP adopts a clustering strategy to build a population-tailored “Mock Reference” from the same GBS data used for downstream SNP calling and genotyping. It may be used to augment the results of alternative analyses, whether or not a reference is available. The tool may complement other reference-based pipelines by extracting more information per sequencing dollar spent. GBS-SNPCROP may be useful even in this case, able to detect large numbers of additional high-quality SNPs missed by the tag-based and read length-restricted approach of TASSEL-GBS.
Afcluster / alignment free clustering
Analyses sequence. Afcluster allows to perform assembly with reduced resources and a minimal loss of quality. It allows soft expectation maximization (EM) clustering, in which case each sequence is only assigned to each cluster with some probability. This method gives some estimate of the clustering accuracy without the overhead of the consensus clustering. The ability to simultaneously assign the same sequence to several clusters is also useful when splitting a sample before performing assembly.
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