SNP-o-matic

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SNP-o-matic

MOTIVATION High throughput sequencing technologies generate large amounts of short reads. Mapping these to a reference sequence consumes large amounts of processing time and memory, and read mapping errors can lead to noisy or incorrect alignments. SNP-o-matic is a fast, memory-efficient and stringent read mapping tool offering a variety of analytical output functions, with an emphasis on genot...

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Template-O-Matic: A toolbox for creating customized pediatric templates

Processing pediatric neuroimaging data is a challenge due to pervasive morphological changes that occur in the human brain during normal development. This is of special relevance when reference data is used as part of the processing approach, as in spatial normalization and tissue segmentation. Current approaches construct reference data (templates) by averaging brain images from a control grou...

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Slim-o-matic: a Semi-Automated Way to Generate Gene Ontology Slims

The Gene Ontology (GO) currently contains over 40,000 terms describing the locations, activities and processes of gene products. Several millions of gene products have been annotated using the GO, and these annotations are routinely used for multiple applications. However, because of the di↵erence of granularity in the annotations, it is useful to summarize GO annotations using GO slims. GO sli...

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Fasta-O-Matic: a tool to sanity check and if needed reformat FASTA files

Background: As the sheer volume of bioinformatic sequence data increases, the only way to take advantage of this content is to more completely automate robust analysis workflows. Analysis bottlenecks are often mundane and overlooked processing steps. Idiosyncrasies in reading and/or writing bioinformatics file formats can halt or impair analysis workflows by interfering with the transfer of dat...

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BEAT-o-matic: a baseline for learning behavior expressions from utterances

In the context of human-robot dialogue, we consider the problem of annotating utterances with non-verbal behaviour. We argue for the need of a data-driven approach, that could learn those behaviours. As a first step we propose to replicate the results obtained by the current state-of-the-art, the rule-based system BEAT. Our proposed baseline, based on multi-label logistic regression with some s...

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2009

ISSN: 1460-2059,1367-4803

DOI: 10.1093/bioinformatics/btp403