A data parallel strategy for aligning multiple biological sequences on multi-core computers

Gradual in vitro Evolution of Cefepime Resistance in an ST131 Escherichia coli Strain Expressing a Plasmid-Encoded CMY-2 β-Lactamase

Background

In a previous report, a clinical ST131 Escherichia coli isolate (Ec-1),producing a plasmid-encoded AmpC β-lactamase CMY-2, evolved in vivo under cefepime (FEP) treatment to the FEP-resistant Ec-2 strain expressing an extended-spectrum β-lactamase CMY-33. To compare factors responsible for in vitro and in vivo FEP resistance, we reproduced in vitro FEP resistance evolution in Ec-1.

Methods

FEP-resistant mutants were generated by subjecting Ec-1 (FEP MIC = 0.125 mg/L) to sub-inhibitory concentrations of FEP. MICs were obtained by broth microdilution or Etest. Strains were sequenced on an Illumina HiSeq platform. Transcriptional levels and plasmid copy numbers were determined by real-time PCR. Outer membrane proteins (OMPs) were extracted and separated by SDS-PAGE. Growth kinetics was evaluated by measuring OD₄₅₀.

Results

The CMY-2 expressed by Ec-1 evolved to a CMY-69 (strain EC-4) by an Ala294Pro substitution after 24 passages. After 30 passages, the FEP MIC increased to 256 mg/L (strain EC-32). SDS PAGE did not reveal any lack of OMPs in the mutant strains. However, bla_CMY transcription levels were up to 14-times higher than in Ec-1, which was partially explained by mutations in the upstream region of repA resulting in a higher copy number of the bla_CMY-harboring IncI1 plasmid. All mutants showed a slight growth defect but no significant difference in relative growth rates compared to Ec-1.

Conclusion

In vitro sub-inhibitory concentrations of FEP resulted in the selection of resistance mutations altering the H-10 helix of the CMY-2 and increasing the plasmid copy number. Appropriate dosing strategies may help preventing resistance evolution during treatments.

A Dictionary-Based Approach for Identifying Biomedical Concepts

In this research, we provided a dictionary-based approach for identifying biomedical concepts from the literature. The approach first crawled experimental corpus by E-utilities and built a concept dictionary. Then, we developed an algorithm called Variable-step Window Identification Algorithm (VWIA) for matching biomedical concepts based on preprocessing, POS tagging and the formation of phrase block. The approach could identify embedded biomedical concepts and new concepts, which could identify concepts more completely. The proposed approach obtain 95.0% F-measure overall for the test dataset. Thus, it is promising for the method of biomedical text mining.

Parallel Implementation of MAFFT on CUDA-Enabled Graphics Hardware

Multiple sequence alignment (MSA) constitutes an extremely powerful tool for many biological applications including phylogenetic tree estimation, secondary structure prediction, and critical residue identification. However, aligning large biological sequences with popular tools such as MAFFT requires long runtimes on sequential architectures. Due to the ever increasing sizes of sequence databases, there is increasing demand to accelerate this task. In this paper, we demonstrate how graphic processing units (GPUs), powered by the compute unified device architecture (CUDA), can be used as an efficient computational platform to accelerate the MAFFT algorithm. To fully exploit the GPU's capabilities for accelerating MAFFT, we have optimized the sequence data organization to eliminate the bandwidth bottleneck of memory access, designed a memory allocation and reuse strategy to make full use of limited memory of GPUs, proposed a new modified-run-length encoding (MRLE) scheme to reduce memory consumption, and used high-performance shared memory to speed up I/O operations. Our implementation tested in three NVIDIA GPUs achieves speedup up to 11.28 on a Tesla K20m GPU compared to the sequential MAFFT 7.015.