Imran S. Haque
Stanford University
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Publication
Featured researches published by Imran S. Haque.
Journal of Physical Chemistry B | 2010
Jay W. Ponder; Chuanjie Wu; Pengyu Ren; Vijay S. Pande; John D. Chodera; Michael J. Schnieders; Imran S. Haque; David L. Mobley; Daniel S. Lambrecht; Robert A. DiStasio; Martin Head-Gordon; Gary N. I. Clark; Margaret E. Johnson; Teresa Head-Gordon
Molecular force fields have been approaching a generational transition over the past several years, moving away from well-established and well-tuned, but intrinsically limited, fixed point charge models toward more intricate and expensive polarizable models that should allow more accurate description of molecular properties. The recently introduced AMOEBA force field is a leading publicly available example of this next generation of theoretical model, but to date, it has only received relatively limited validation, which we address here. We show that the AMOEBA force field is in fact a significant improvement over fixed charge models for small molecule structural and thermodynamic observables in particular, although further fine-tuning is necessary to describe solvation free energies of drug-like small molecules, dynamical properties away from ambient conditions, and possible improvements in aromatic interactions. State of the art electronic structure calculations reveal generally very good agreement with AMOEBA for demanding problems such as relative conformational energies of the alanine tetrapeptide and isomers of water sulfate complexes. AMOEBA is shown to be especially successful on protein-ligand binding and computational X-ray crystallography where polarization and accurate electrostatics are critical.
grid computing | 2010
Imran S. Haque; Vijay S. Pande
Graphics processing units (GPUs) are gaining widespread use in high-performance computing because of their performance advantages relative to CPUs. However, the reliability of GPUs is largely unproven. In particular, current GPUs lack error checking and correcting (ECC) in their memory subsystems. The impact of this design has not been previously measured at a large enough scale to quantify soft error events. We present MemtestG80, our software for assessing memory error rates on NVIDIA graphics cards. Furthermore, we present a large-scale assessment of GPU error rate, conducted by running MemtestG80 on over 50,000 hosts on the Folding@home distributed computing network. Our control experiments on consumer-grade and dedicated-GPGPU hardware in a controlled environment found no errors. However, our survey on Folding@home finds that, in their installed environments, two-thirds of tested GPUs exhibit a detectable, pattern-sensitive rate of memory soft errors. We show that these errors persist after controlling for over clocking and environmental proxies for temperature, but depend strongly on board architecture.
Journal of Computational Chemistry | 2010
Imran S. Haque; Vijay S. Pande
Modern graphics processing units (GPUs) are flexibly programmable and have peak computational throughput significantly faster than conventional CPUs. Herein, we describe the design and implementation of PAPER, an open‐source implementation of Gaussian molecular shape overlay for NVIDIA GPUs. We demonstrate one to two order‐of‐magnitude speedups on high‐end commodity GPU hardware relative to a reference CPU implementation of the shape overlay algorithm and speedups of over one order of magnitude relative to the commercial OpenEye ROCS package. In addition, we describe errors incurred by approximations used in common implementations of the algorithm.
Journal of Chemical Information and Modeling | 2011
Imran S. Haque; Vijay S. Pande; W. Patrick Walters
Similarity measures based on the comparison of dense bit vectors of two-dimensional chemical features are a dominant method in chemical informatics. For large-scale problems, including compound selection and machine learning, computing the intersection between two dense bit vectors is the overwhelming bottleneck. We describe efficient implementations of this primitive as well as example applications using features of modern CPUs that allow 20-40× performance increases relative to typical code. Specifically, we describe fast methods for population count on modern x86 processors and cache-efficient matrix traversal and leader clustering algorithms that alleviate memory bandwidth bottlenecks in similarity matrix construction and clustering. The speed of our 2D comparison primitives is within a small factor of that obtained on GPUs and does not require specialized hardware.
Journal of Chemical Information and Modeling | 2010
Imran S. Haque; Vijay S. Pande; W. Patrick Walters
LINGOs are a holographic measure of chemical similarity based on text comparison of SMILES strings. We present a new algorithm for calculating LINGO similarities amenable to parallelization on SIMD architectures (such as GPUs and vector units of modern CPUs). We show that it is nearly 3x as fast as existing algorithms on a CPU, and over 80x faster than existing methods when run on a GPU.
Seminars in Perinatology | 2016
Gabriel A. Lazarin; Imran S. Haque
Carrier screening is the practice of testing individuals to identify those at increased risks of having children affected by genetic diseases. Professional guidelines on carrier screening have been available for more than 15 years, and have historically targeted specific diseases that occur at increased frequencies in defined ethnic populations. Enabled by rapidly evolving technology, expanded carrier screening aims to identify carriers for a broader array of diseases and may be applied universally (equally across all ethnic groups). This new approach deviates from the well-established criteria for screening models. In this review, we summarize the rationale for expanded carrier screening using available literature regarding clinical and technical data, as well as provider perspectives. We also discuss important avenues for further research in this burgeoning field.
PeerJ | 2016
Hyunseok P. Kang; Jared R. Maguire; Clement S. Chu; Imran S. Haque; Henry Lai; Rebecca Mar-Heyming; Kaylene Ready; Valentina S. Vysotskaia; Eric A. Evans
Hereditary breast and ovarian cancer syndrome, caused by a germline pathogenic variant in the BRCA1 or BRCA2 (BRCA1/2) genes, is characterized by an increased risk for breast, ovarian, pancreatic and other cancers. Identification of those who have a BRCA1/2 mutation is important so that they can take advantage of genetic counseling, screening, and potentially life-saving prevention strategies. We describe the design and analytic validation of the Counsyl Inherited Cancer Screen, a next-generation-sequencing-based test to detect pathogenic variation in the BRCA1 and BRCA2 genes. We demonstrate that the test is capable of detecting single-nucleotide variants (SNVs), short insertions and deletions (indels), and copy-number variants (CNVs, also known as large rearrangements) with zero errors over a 114-sample validation set consisting of samples from cell lines and deidentified patient samples, including 36 samples with BRCA1/2pathogenic germline mutations.
Journal of Chemical Information and Modeling | 2010
Imran S. Haque; Vijay S. Pande
Algorithms for several emerging large-scale problems in cheminformatics have as their rate-limiting step the evaluation of relatively slow chemical similarity measures, such as structural similarity or three-dimensional (3-D) shape comparison. In this article we present SCISSORS, a linear-algebraical technique (related to multidimensional scaling and kernel principal components analysis) to rapidly estimate chemical similarities for several popular measures. We demonstrate that SCISSORS faithfully reflects its source similarity measures for both Tanimoto calculation and rank ordering. After an efficient precalculation step on a database, SCISSORS affords several orders of magnitude of speedup in database screening. SCISSORS furthermore provides an asymptotic speedup for large similarity matrix construction problems, reducing the number of conventional slow similarity evaluations required from quadratic to linear scaling.
Genetics in Medicine | 2018
Kyle A. Beauchamp; Dale Muzzey; Kenny K. Wong; Gregory J. Hogan; Kambiz Karimi; Sophie I Candille; Nikita Mehta; Rebecca Mar-Heyming; K Eerik Kaseniit; H. Peter Kang; Eric A. Evans; James D. Goldberg; Gabriel A. Lazarin; Imran S. Haque
PurposeThe recent growth in pan-ethnic expanded carrier screening (ECS) has raised questions about how such panels might be designed and evaluated systematically. Design principles for ECS panels might improve clinical detection of at-risk couples and facilitate objective discussions of panel choice.MethodsGuided by medical-society statements, we propose a method for the design of ECS panels that aims to maximize the aggregate and per-disease sensitivity and specificity across a range of Mendelian disorders considered serious by a systematic classification scheme. We evaluated this method retrospectively using results from 474,644 de-identified carrier screens. We then constructed several idealized panels to highlight strengths and limitations of different ECS methodologies.ResultsBased on modeled fetal risks for “severe” and “profound” diseases, a commercially available ECS panel (Counsyl) is expected to detect 183 affected conceptuses per 100,000 US births. A screen’s sensitivity is greatly impacted by two factors: (i) the methodology used (e.g., full-exon sequencing finds more affected conceptuses than targeted genotyping) and (ii) the detection rate of the screen for diseases with high prevalence and complex molecular genetics (e.g., fragile X syndrome).ConclusionThe described approaches enable principled, quantitative evaluation of which diseases and methodologies are appropriate for pan-ethnic expanded carrier screening.
PeerJ | 2017
Valentina S. Vysotskaia; Gregory J. Hogan; Genevieve M. Gould; Xin Wang; Alexander De Jong Robertson; Kevin R. Haas; Mark R. Theilmann; Lindsay Spurka; Peter V. Grauman; Henry H. Lai; Diana Jeon; Genevieve Haliburton; Matt Leggett; Clement S. Chu; Kevin Iori; Jared R. Maguire; Kaylene Ready; Eric A. Evans; Hyunseok P. Kang; Imran S. Haque
The past two decades have brought many important advances in our understanding of the hereditary susceptibility to cancer. Numerous studies have provided convincing evidence that identification of germline mutations associated with hereditary cancer syndromes can lead to reductions in morbidity and mortality through targeted risk management options. Additionally, advances in gene sequencing technology now permit the development of multigene hereditary cancer testing panels. Here, we describe the 2016 revision of the Counsyl Inherited Cancer Screen for detecting single-nucleotide variants (SNVs), short insertions and deletions (indels), and copy number variants (CNVs) in 36 genes associated with an elevated risk for breast, ovarian, colorectal, gastric, endometrial, pancreatic, thyroid, prostate, melanoma, and neuroendocrine cancers. To determine test accuracy and reproducibility, we performed a rigorous analytical validation across 341 samples, including 118 cell lines and 223 patient samples. The screen achieved 100% test sensitivity across different mutation types, with high specificity and 100% concordance with conventional Sanger sequencing and multiplex ligation-dependent probe amplification (MLPA). We also demonstrated the screen’s high intra-run and inter-run reproducibility and robust performance on blood and saliva specimens. Furthermore, we showed that pathogenic Alu element insertions can be accurately detected by our test. Overall, the validation in our clinical laboratory demonstrated the analytical performance required for collecting and reporting genetic information related to risk of developing hereditary cancers.