Hardware accelerators for biocomputing: A survey

Sarkar, S.; Majumder, Turbo; Kalyanaraman, Ananth; Pande, Partha Pratim

doi:10.1109/iscas.2010.5537736

Cited by 40 publications

(20 citation statements)

References 22 publications

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“…An alternative formulation of this approach, which was perhaps undertaken to reduce computational complexity, would be predicated on a multivariate spline of all predictors (Chui, 1987). We have indeed found regression modeling on typical ChIP-seq datasets to be computationally cumbersome on single to quad-core processors and have opted for implementations utilizing GPU computing (Sarkar et al, 2010). Lastly, like BayesPeak, it is unclear whether MOSAiCS will accommodate zero-inflation in two-sample analysis.…”

Section: Testing the Poisson Assumptionmentioning

confidence: 99%

Normalization, bias correction, and peak calling for ChIP-seq

Diaz

Park²,

Lim³

et al. 2012

Statistical Applications in Genetics and Molecular Biology

103

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Next-generation sequencing is rapidly transforming our ability to profile the transcriptional, genetic, and epigenetic states of a cell. In particular, sequencing DNA from the immunoprecipitation of protein-DNA complexes (ChIP-seq) and methylated DNA (MeDIP-seq) can reveal the locations of protein binding sites and epigenetic modifications. These approaches contain numerous biases which may significantly influence the interpretation of the resulting data. Rigorous computational methods for detecting and removing such biases are still lacking. Also, multi-sample normalization still remains an important open problem. This theoretical paper systematically characterizes the biases and properties of ChIP-seq data by comparing 62 separate publicly available datasets, using rigorous statistical models and signal processing techniques. Statistical methods for separating ChIP-seq signal from background noise, as well as correcting enrichment test statistics for sequence-dependent and sonication biases, are presented. Our method effectively separates reads into signal and background components prior to normalization, improving the signal-to-noise ratio. Moreover, most peak callers currently use a generic null model which suffers from low specificity at the sensitivity level requisite for detecting subtle, but true, ChIP enrichment. The proposed method of determining a cell type-specific null model, which accounts for cell type-specific biases, is shown to be capable of achieving a lower false discovery rate at a given significance threshold than current methods.

show abstract

Section: Testing the Poisson Assumptionmentioning

confidence: 99%

Normalization, bias correction, and peak calling for ChIP-seq

Diaz

Park²,

Lim³

et al. 2012

Statistical Applications in Genetics and Molecular Biology

103

View full text Add to dashboard Cite

show abstract

“…The use of enhanced sampling techniques like parallel tempering, umbrella sampling, or metadynamics, has been proposed as a solution to this issue (Burney & Pfaendtner, 2013). Using special-purpose machines for MD simulations (eg, Anton (Shaw et al, 2008)) or hardware accelerators using platforms such as field-programmable gate arrays (FPGAs), graphics processing units (GPUs), the Cell Broadband Engine (CBE), and multicore processors (Sarkar, Majumder, Kalyanaraman, & Pande, 2010) is another potential solution.…”

Section: Perspective: Challenges and Future Directionsmentioning

confidence: 99%

Using Molecular Simulation to Study Biocatalysis in Ionic Liquids

Sprenger

Pfaendtner

2016

Methods in Enzymology

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“…Even though it tends to be very expensive to develop such true heterogeneous infrastructure, the choice is purely based on the user Several other works such as (Chase et al, 2008), (Fowers et al, 2012), (Grozea et al, 2010), (Haselman et al, 2012), (Jones et al, 2010), (Kapre et al, 2009), (Marrakchi et al, 2012), (Muthumala et al, 2012), (Nechma et al, 2012), (Pacholik et al, 2011), (Sarkar et al, 2010), (Yang et al, 2010) and (Zhang et al, 2009) have performed a comparison on above hardware platforms leading to similar conclusions.…”

Section: Performance and Power Efficiency In Gpu And Fpgamentioning

confidence: 99%

A survey on big data processing infrastructure: evolving role of FPGA

Nunna

Mehdipour

Trouvé

et al. 2015

IJBDI

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In today's commercial world, information is becoming a major economic resource thus leading to a statement -Information is wealth. It is a technical challenge for computer systems in managing and analyzing the large volumes of data coming from a variety of resources continuously over a period. Experts are in a mood of moving towards alternative hardware platforms for achieving high-speed data processing and analysis especially for streaming applications. In this paper, (a) existing trends in big data processing and the necessary systems involved are studied by performing a survey on available platforms, (b) recommended features and suitable hardware systems are proposed based on the operations involved in the processing. Investigation shows that, in combination with CPU and along with GPU, FPGA is a possible alternative. It can be a part of the heterogeneous platform featuring parallelism, pipelining and high performance for the operations involved in big data processing.

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Hardware accelerators for biocomputing: A survey

Cited by 40 publications

References 22 publications

Normalization, bias correction, and peak calling for ChIP-seq

Normalization, bias correction, and peak calling for ChIP-seq

Using Molecular Simulation to Study Biocatalysis in Ionic Liquids

A survey on big data processing infrastructure: evolving role of FPGA

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