Bio Saga Blog - A Chronicle of Life Sciences & Informatics

At the Bio-IT World Conference last week, Intel unveiled a new website, dubbed Optimized Code, that offers access to versions of several popular open-source bioinformatics analysis tools that the company has optimized to run on Intel Xeon processors, with the aim of generating results faster and more efficiently than standard iterations of these solutions. The first set of applications that the company has released specifically for genomic analysis include optimized versions of  the Broad Institute's Genome Analysis Toolkit ; Blast algorithms for nucleotide- and protein-based sequence searching; BWA-ALN , software for mapping low-divergent sequences to a reference genome; and MPI-HMMER , protein sequence analysis software. The company has also released optimized code for AMBER and NAMD, both of which are used for simulating the molecular dynamics of biomolecular systems. The Intel website provides performance numbers for each of the optimized codes as well as directions for h...

In structural biology, similarity analysis of protein structure is a crucial step in studying the relationship between proteins. Despite the considerable number of techniques that have been explored within the past two decades, the development of new alternative methods is still an active research area due to the need for high performance tools. Results: In this paper, we present TS-AMIR, a Topology String Alignment Method for Intensive Rapid comparison of protein structures. The proposed method works in two stages: In the first stage, the method generates a topology string based on the geometric details of secondary structure elements, and then, utilizes an n-gram modelling technique over entropy concept to capture similarities in these strings. This initial correspondence map between secondary structure elements is submitted to the second stage in order to obtain the alignment at the residue level. Applying the Kabsch method, a heuristic step-by-step algorithm is adopted in the secon...

Abstract Whole genome amplification by the multiple displacement amplification (MDA) method allows sequencing of DNA from single cells of bacteria that cannot be cultured. Assembling a genome is challenging, however, because MDA generates highly nonuniform coverage of the genome. Here we describe an algorithm tailored for short-read data from single cells that improves assembly through the use of a progressively increasing coverage cutoff. Assembly of reads from single  Escherichia coli  and  Staphylococcus aureus  cells captures >91% of genes within contigs, approaching the 95% captured from an assembly based on many  E. coli  cells. We apply this method to assemble a genome from a single cell of an uncultivated SAR324 clade of Deltaproteobacteria, a cosmopolitan bacterial lineage in the global ocean. Metabolic reconstruction suggests that SAR324 is aerobic, motile and chemotaxic. Our approach enables acquisition of genome assemblies for individual uncu...

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...

In a study appearing in the newest issue of the  Archives of Neurology , members of the Texas Alzheimer's Research Consortium reported that they have  identified protein biomarkers  in the blood that can be used to distinguish between individuals with and without Alzheimer's disease. The researchers compared protein patterns in blood samples from hundreds of individuals with or without Alzheimer's disease and incorporated these potential biomarkers into an algorithm for detecting Alzheimer's cases in a test group. Their results so far suggest this algorithm can accurately classify most Alzheimer's cases — particularly when combined with  APOE  status and demographic data. And, they said, information from the biomarker study is offering clues about possible sub-groups within Alzheimer's. For instance, by looking at some of the proteins that are frequently expressed at different levels in the blood of those with Alzheimer's disease, the team identified a pote...

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/9/82 Abstract Background The performance of different programming languages has previously been benchmarked using abstract mathematical algorithms, but not using standard bioinformatics algorithms. We compared the memory usage and speed of execution for three standard bioinformatics methods, implemented in programs using one of six different programming languages. Programs for the Sellers algorithm, the Neighbor-Joining tree construction algorithm and an algorithm for parsing BLAST file outputs were implemented in C, C++, C#, Java, Perl and Python. Results Implementations in C and C++ were fastest and used the least memory. Programs in these languages generally contained more lines of code. Java and C# appeared to be a compromise between the flexibility of Perl and Python and the fast performance of C and C++. The relative performance of the tested languages d...