Biology subjects

Porter, J.

Publications and source records attributed to Porter, J..

4 recordsLinked to original sources

bioRxiv2018

Enspara: Modeling molecular ensembles with scalable data structures and parallel computing

Markov state models (MSMs) are quantitative models of protein dynamics that are useful for uncovering the structural fluctuations that proteins undergo, as well as the mechanisms of these conformational changes. Given the enormity of conformational space, there has been ongoing interest in identifying a small number of states that capture the essential features of a protein. Generally, this is achieved by making assumptions about the properties of relevant features--for example, that the most important features are those that change slowly. An alternative strategy is to keep as many degrees of freedom as possible and subsequently learn from the model which of the features are most important. In these larger models, however, traditional approaches quickly become computationally intractable. In this paper, we present enspara, a library for working with MSMs that provides several novel algorithms and specialized data structures that dramatically improve the scalability of traditional MSM methods. This includes ragged arrays for minimizing memory requirements, MPI-parallelized implementations of compute-intensive operations, and a flexible framework for model estimation.

biophysics↗

bioRxiv2018

BisPin and BFAST-Gap: Mapping Bisulfite-Treated Reads

BackgroundBisPin is a new multiprocess bisulfite-treated short DNA read mapper written in Python 2.7. It performs alignments using BFAST, leveraging its multithreading functionality and thorough hash-based indexing strategy. BisPin is feature rich and supports directional, nondirectional, PBAT, and hairpin construction strategies. BisPin approaches read mapping by converting the Cs to Ts and the Gs to As in both the reads and the reference genome. BisPin uses fast rescoring to disambiguate ambiguously aligned reads for a superior amount of uniquely mapped reads compared to other mappers. The performance of BisPin was evaluated on both real and simulated data in comparison to other read mappers.\n\nBFAST-Gap is a modified version of BFAST meant for Ion Torrent reads. It uses a parameterized logistic function to determine the weights of the gap open and extension penalties based on the homopolymer run length of the DNA read. This is because the Ion Torrent sequencing technology can overcall and undercall homopolymer runs. BisPin works with both BFAST-Gap and BFAST. BFAST-Gap is compatible with indexes built with BFAST. There are few mappers that specifically address Ion Torrent data. BFAST-Gap works with Illumina reads as well.\n\nResultsBisPin with BFAST consistently had a higher amount of uniquely mapped reads compared to other mappers on real data using a variety of construction strategies. Using a hairpin validation strategy, BisPin was superior using the maximum score, and it mapped 73% of reads correctly.\n\nBisPin with BFAST-Gap on Ion Torrent reads with a logistic gap open penalty function improved mapping accuracy with real and simulated data. On simulated bisulfite Ion Torrent data, the area under the curve was improved by approximately seven, and on one real data set, the uniquely mapped percent was improved by seven percent. BFAST-Gap performed better than TMAP on simulated regular Ion Torrent reads, and TMAP is designed for Ion Torrent reads. Other read mappers had worse performance.\n\nConclusionsBisPin and BFAST-Gap have consistently good accuracy with a variety of data. BisPin is feature-rich. This makes BisPin and BFAST-Gap useful additions to read mapping software.

bioinformatics↗

bioRxiv2018

Fast NeighborNet: Improving the Speed of the Neighbor-Net Phylogenetic Network Algorithm with Multithreading and a Relaxed Search Strategy

Fast Neighbor-Net is a command-line Java program that has fast implementations of the popular Neighbor-Net phylo-genetic split network approach. This allows more efficiency in computationally intensive tasks such as larger scale data analysis and bootstrapping. The canonical search phase iteratively finds a pair of connected components that minimizes a distance function in {Theta}(n3) time in the input taxa count. A relaxed search strategy has been implemented that has averagecase time complexity of[O] (n2 log n) but with {Theta}(n3) worst-case time complexity. This search strategy sacrifices some accuracy for speed. The original approachs implementation has been improved by using good programming practice. These improvements increased run-time performance by a constant factor of approximately 2 and reduced memory requirements by a constant factor of approximately 6. These search strategies allow multithreading to better use modern CPU hardware. PFAM data of 2000-30,000 taxa were used for testing performance. The canonical implementation with three threads improved average performance by approximately 2.1. The relaxed search has good quality, and the accuracy was tested on a mammal and a eukaryote data set. Kendall tau distance was used as a rough measure of topological similarity for the relaxed and canonical search strategies.

bioinformatics↗

bioRxiv2017

InfoTrim: A DNA Read Quality Trimmer Using Entropy

Biological DNA reads are often trimmed before mapping, genome assembly, and other tasks to improve the quality of the results. Biological sequence complexity relates to alignment quality as low complexity regions can align poorly. There are many read trimmers, but many do not use sequence complexity for trimming. Alignment of reads generated from whole genome bisulfite sequencing is especially challenging since bisulfite treated reads tend to reduce sequence complexity. InfoTrim, a new read trimmer, was created to explore these issues. It is evaluated against five other trimmers using four read mappers on real and simulated bisulfite treated DNA data. InfoTrim produces reasonable results consistent with other trimmers.

bioinformatics↗