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Genetic Analysis: GWAS Genetic Variation Linkage Analysis Gene Expression
Sequence Analysis: Assembly Annotation Alignment RNAi RNA-seq Primer Design Next Generation Tools Phylogenetic Analysis Gene Structure Viral
AssembleViral454 is a new assembler, based on the ARACHNE package, designed for small and non-repetitive genomes sequenced at high depth. It was specifically designed to assemble read data generated from a mixed population of viral genomes. Reads need not be paired, and it is assumed that no sequence repeat in the genome would be large enough to fully contain an average read. The assembly process consists of two steps: First, a pre-processing stage is run, the output of which is an initial read layout. This is identical to the process employed in the published ARACHNE algorithm. This stage generally results in a fragmented assembly. Second, we employ an iterative procedure that incrementally merges contigs and improves read placement.
ComSeq is a tool for designing and analyzing combinatorial pooling experiments for next-generation sequencing projects using compressed sensing technology. ComSeq enables the identification of novel rare alleles, as well as detection of individuals who are carriers of known SNPs in a large population in an efficient manner.
Automation of the Illumina TruSeq Exome Enrichment protocol on an Agilent Bravo liquid handling platform.
The Genome Analysis Toolkit (GATK) is a structured programming framework designed to enable the rapid development of efficient and robust analysis tools for next-generation DNA sequencers. The GATK solves the data management challenge by separating data access patterns from analysis algorithms, using the functional programming philosophy of Map/Reduce. Since the GATK’s traversal engine encapsulates the complexity of efficiently accessing the next-generation sequencing data, researchers and developers are free to focus on their specific analysis algorithms. This not only vastly improves the productivity of developers, who can quickly write new analyses, but also results in tools that are efficient and robust, and can benefit from improvements to a common data management engine.
RC454 is a program that takes a set of 454 read and quality files as well as a consensus assembly for those reads and corrects for known 454 error modes such as homopolymer indels and carry forward/incomplete extension (CAFIE). It will also correct for any indel that breaks the reading frame, unless it occurs in more than 25% of the reads. Since the algorithm is aggressive in correcting for errors, it is important to align the reads to their own assembly rather than to an external reference to prevent misalignments as much as possible. RC454 uses Mosaik to align the corrected reads between each step, and as such it is required to run the script.
V-FAT is a tool to perform automated computational finishing and annotation of de novo viral assemblies. V-FAT uses reference and read data to order and merge contigs, correct frameshifts, and produce NCBI-ready annotation files. It also performs a set of quality assurance measurements including coverage computation by gene or amplicon and identification of potential consensus errors.
V-Phaser is a tool to call variants in mixed populations from ultra-deep sequence data. V-Phaser combines information regarding the covariation (i.e. phasing) between observed variants to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates base quality scores to increase specificity. V-Phaser can reliably detect rare variants in mixed populations that occur at frequencies of <1%. The V-Phaser package also includes V-Profiler a tool to analyze and visualize variants.
VICUNA is a de novo assembly program targeting populations with high mutation rates. It creates a single linear representation of the mixed population on which intra-host variants can be mapped. For clinical samples rich in contamination (e.g., >95%), VICUNA can leverage existing genomes, if available, to assemble only target-alike reads. After initial assembly, it can also use existing genomes to perform guided merging of contigs. For each data set (e.g., Illumina paired read, 454), VICUNA outputs consensus sequence(s) and the corresponding multiple sequence alignment of constituent reads. VICUNA efficiently handles ultra-deep sequence data with tens of thousands fold coverage.