Comparison of linear gap penalties and profile-based variable gap penalties in profile–profile alignments

SneakySnake: a fast and accurate universal genome pre-alignment filter for CPUs, GPUs and FPGAs

Motivation

We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for computationally costly sequence alignment. The key idea of SneakySnake is to reduce the approximate string matching (ASM) problem to the single net routing (SNR) problem in VLSI chip layout. In the SNR problem, we are interested in finding the optimal path that connects two terminals with the least routing cost on a special grid layout that contains obstacles. The SneakySnake algorithm quickly solves the SNR problem and uses the found optimal path to decide whether or not performing sequence alignment is necessary. Reducing the ASM problem into SNR also makes SneakySnake efficient to implement on CPUs, GPUs and FPGAs.

Results

SneakySnake significantly improves the accuracy of pre-alignment filtering by up to four orders of magnitude compared to the state-of-the-art pre-alignment filters, Shouji, GateKeeper and SHD. For short sequences, SneakySnake accelerates Edlib (state-of-the-art implementation of Myers’s bit-vector algorithm) and Parasail (state-of-the-art sequence aligner with a configurable scoring function), by up to 37.7× and 43.9× (>12× on average), respectively, with its CPU implementation, and by up to 413× and 689× (>400× on average), respectively, with FPGA and GPU acceleration. For long sequences, the CPU implementation of SneakySnake accelerates Parasail and KSW2 (sequence aligner of minimap2) by up to 979× (276.9× on average) and 91.7× (31.7× on average), respectively. As SneakySnake does not replace sequence alignment, users can still obtain all capabilities (e.g. configurable scoring functions) of the aligner of their choice, unlike existing acceleration efforts that sacrifice some aligner capabilities.

Availabilityand implementation

https://github.com/CMU-SAFARI/SneakySnake.

Supplementary information

Supplementary data are available at Bioinformatics online.

Shouji: a fast and efficient pre-alignment filter for sequence alignment

MOTIVATION

The ability to generate massive amounts of sequencing data continues to overwhelm the processing capability of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the execution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We introduce Shouji, a highly parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator that adopts modern field-programmable gate array (FPGA) architectures to further boost the performance of our algorithm.

RESULTS

Shouji significantly improves the accuracy of pre-alignment filtering by up to two orders of magnitude compared to the state-of-the-art pre-alignment filters, GateKeeper and SHD. Our FPGA-based accelerator is up to three orders of magnitude faster than the equivalent CPU implementation of Shouji. Using a single FPGA chip, we benchmark the benefits of integrating Shouji with five state-of-the-art sequence aligners, designed for different computing platforms. The addition of Shouji as a pre-alignment step reduces the execution time of the five state-of-the-art sequence aligners by up to 18.8×. Shouji can be adapted for any bioinformatics pipeline that performs sequence alignment for verification. Unlike most existing methods that aim to accelerate sequence alignment, Shouji does not sacrifice any of the aligner capabilities, as it does not modify or replace the alignment step.

AVAILABILITY AND IMPLEMENTATION

https://github.com/CMU-SAFARI/Shouji.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of WD40 repeats by secondary structure-aided profile-profile alignment

A WD40 protein typically contains four or more repeats of ~40 residues ended with the Trp-Asp dipeptide, which folds into β-propellers with four β strands in each repeat. They often function as scaffolds for protein-protein interactions and are involved in numerous fundamental biological processes. Despite their important functional role, the "velcro" closure of WD40 propellers and the diversity of WD40 repeats make their identification a difficult task. Here we develop a new WD40 Repeat Recognition method (WDRR), which uses predicted secondary structure information to generate candidate repeat segments, and further employs a profile-profile alignment to identify the correct WD40 repeats from candidate segments. In particular, we design a novel alignment scoring function that combines dot product and BLOSUM62, thereby achieving a great balance of sensitivity and accuracy. Taking advantage of these strategies, WDRR could effectively reduce the false positive rate and accurately identify more remote homologous WD40 repeats with precise repeat boundaries. We further use WDRR to re-annotate the Pfam families in the β-propeller clan (CL0186) and identify a number of WD40 repeat proteins with high confidence across nine model organisms. The WDRR web server and the datasets are available at http://protein.cau.edu.cn/wdrr/.