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Showing posts with the label sequence alignment

Microbial Genomes Curator @ Computercraft Corporation--Maryland (US)

Microbial Genomes Curator @ Computercraft Corporation--Maryland (US).  Submitted by Computercraft Corporation; posted on Saturday, March 17, 2012 RESPONSIBILITIES: Computercraft seeks a microbiologist to work with a team of software developers and biologists on microbial genome analysis including pan-genome, protein clusters, phylogenetic tree and more. This is a technically challenging position requiring experience in genome sequencing and annotation. A background in comparative genome analysis such as alignments and tree building is a plus. Our scientists work with genomic experts at the NIH's National Center for Biotechnology Information (NCBI) to create and enhance a suite of databases and tools available to researchers worldwide. Teamwork interaction and excellent organizational skills are essential for this detail-oriented position, as is scientific problem-solving with a results-oriented focus. REQUIREMENTS: * PhD in molecular biology, microbiology, or related field * ...

New Algorithm for detection of viral sequence fragments of HIV-1 subfamilies

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Background Methods of determining whether or not any particular HIV-1 sequence stems - completely or in part - from some unknown HIV-1 subtype are important for the design of vaccines and molecular detection systems, as well as for epidemiological monitoring. Nevertheless, a single algorithm only, the Branching Index (BI), has been developed for this task so far. Moving along the genome of a query sequence in a sliding window, the BI computes a ratio quantifying how closely the query sequence clusters with a subtype clade. In its current version, however, the BI does not provide predicted boundaries of unknown fragments. Results We have developed  Unknown Subtype Finder  (USF), an algorithm based on a probabilistic model, which automatically determines which parts of an input sequence originate from a subtype yet unknown. The underlying model is based on a simple profile hidden Markov model (pHMM) for each  known  subtype and an additional pHMM for an  unknown...