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BACKGROUND A number of methods are now available to perform automatic assignment of periodic secondary structures from atomic coordinates, based on different characteristics of the secondary structures. In general these methods exhibit a broad consensus as to the location of most helix and strand core segments in protein structures. However the termini of(More)
We address the problem of protein secondary structure prediction with Hidden Markov Models. A 21-state model is built using biological knowledge and statistical analysis of sequence motifs in regular secondary structures. Sequence family information is integrated via the combination of independent predictions of homologous sequences and a weighting scheme.(More)
Mapping short reads against a reference genome is classically the first step of many next-generation sequencing data analyses, and it should be as accurate as possible. Because of the large number of reads to handle, numerous sophisticated algorithms have been developped in the last 3 years to tackle this problem. In this article, we first review the(More)
BACKGROUND Propionibacterium freudenreichii is essential as a ripening culture in Swiss-type cheeses and is also considered for its probiotic use. This species exhibits slow growth, low nutritional requirements, and hardiness in many habitats. It belongs to the taxonomic group of dairy propionibacteria, in contrast to the cutaneous species P. acnes. The(More)
Arthrobacter arilaitensis is one of the major bacterial species found at the surface of cheeses, especially in smear-ripened cheeses, where it contributes to the typical colour, flavour and texture properties of the final product. The A. arilaitensis Re117 genome is composed of a 3,859,257 bp chromosome and two plasmids of 50,407 and 8,528 bp. The(More)
BACKGROUND Current classification of protein folds are based, ultimately, on visual inspection of similarities. Previous attempts to use computerized structure comparison methods show only partial agreement with curated databases, but have failed to provide detailed statistical and structural analysis of the causes of these divergences. RESULTS We(More)
BACKGROUND Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been(More)
Proteins are major constituents of living cells, forming many cellular components and most enzymes. So, knowledge of 3D protein structures is essential to understand biological mechanisms. Researchers often use neural networks to predict secondary structure in proteins, but the networks can be hard to interpret. This alternative method uses an optimal and(More)
BACKGROUND Formal classification of a large collection of protein structures aids the understanding of evolutionary relationships among them. Classifications involving manual steps, such as SCOP and CATH, face the challenge of increasing volume of available structures. Automatic methods such as FSSP or Dali Domain Dictionary, yield divergent(More)