BMF: Bitmapped Mass Fingerprinting for Fast Protein Identification

Yu, Weikuan; Wu, Kesheng; Ku, Wei‐Shinn; Xu, Cong; Gao, Juan

doi:10.1109/cluster.2011.11

Cited by 3 publications

(1 citation statement)

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“…Genomic analysis is the process of analyzing an organism's sequences (such as DNA) to understand its features, functions, structure, or evolution. Methodologies used include sequence alignment, searches against biological databases, etc [31,32].…”

Section: Dna Sequence Analysis and Biopig Toolkitmentioning

confidence: 99%

A case study of tuning MapReduce for efficient Bioinformatics in the cloud

Shi

Wang

et al. 2017

Parallel Computing

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The combination of the Hadoop MapReduce programming model and cloud computing allows biological scientists to analyze next-generation sequencing (NGS) data in a timely and cost-effective manner. Cloud computing platforms remove the burden of IT facility procurement and management from end users and provide ease of access to Hadoop clusters. However, biological scientists are still expected to choose appropriate Hadoop parameters for running their jobs. More importantly, the available Hadoop tuning guidelines are either obsolete or too general to capture the particular characteristics of bioinformatics applications. In this study, we aim to minimize the cloud computing cost spent on bioinformatics data analysis by optimizing the extracted significant Hadoop parameters. When using MapReduce-based bioinformatics tools in the cloud, the default settings often lead to resource underutilization and wasteful expenses. We choose k-mer counting, a representative application used in a large number of NGS data analysis tools, as our study case. Experimental results show that, with the fine-tuned parameters, we achieve a total of 4x speedup compared with the original performance (using the default settings). This paper presents an exemplary case for tuning MapReduce-based bioinformatics applications in the cloud, and documents the key parameters that could lead to significant performance benefits.

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Section: Dna Sequence Analysis and Biopig Toolkitmentioning

confidence: 99%

A case study of tuning MapReduce for efficient Bioinformatics in the cloud

Shi

Wang

et al. 2017

Parallel Computing

Self Cite

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IsAProteinDB: An Indexed Database of Trypsinized Proteins for Fast Peptide Mass Fingerprinting

d’Acierno

2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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In peptite mass fingerprinting, an unknown protein is fragmented into smaller peptides whose masses are accurately measured; the obtained list of weights is then compared with a reference database to obtain a set of matching proteins. The exponential growth of known proteins discourage the use of brute force methods, where the weights' list is compared with each protein in the reference collection; luckily, the scientific literature in the database field highlights that well designed searching algorithms, coupled with a proper data organization, allow to quickly solve the identification problem even on standard desktop computers. In this paper, IsAProteinsDB, an indexed database of trypsinized proteins, is presented. The corresponding search algorithm shows a time complexity that does not significantly depends on the size of the reference protein database.

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