arXiv subjects

Rayan Chikhi

Publications and source records attributed to Rayan Chikhi.

11 recordsLinked to original sources

arXiv2025

Processing-in-memory for genomics workloads

Low-cost, high-throughput DNA and RNA sequencing (HTS) data is the backbone of the life sciences. Genome sequencing is now becoming a part of Predictive, Preventive, Personalized, and Participatory (termed 'P4') medicine. All genomic data are currently processed in energy-hungry computer clusters and centers, necessitating data transfer, consuming substantial energy, and wasting valuable time. Therefore, there is a need for fast, energy-efficient, and cost-efficient technologies that enable genomics research without requiring data centers and cloud platforms. We recently launched the BioPIM Project to leverage emerging processing-in-memory (PIM) technologies to enable energy- and cost-efficient analysis of bioinformatics workloads. The BioPIM Project focuses on co-designing algorithms and data structures commonly used in genomics with several PIM architectures to achieve the highest cost, energy, and time savings.

q-bio.GN↗

arXiv2020

Data structures to represent a set of k-long DNA sequences

The analysis of biological sequencing data has been one of the biggest applications of string algorithms. The approaches used in many such applications are based on the analysis of k-mers, which are short fixed-length strings present in a dataset. While these approaches are rather diverse, storing and querying a k-mer set has emerged as a shared underlying component. A set of k-mers has unique features and applications that, over the last ten years, have resulted in many specialized approaches for its representation. In this survey, we give a unified presentation and comparison of the data structures that have been proposed to store and query a k-mer set. We hope this survey will serve as a resource for researchers in the field as well as make the area more accessible to researchers outside the field.

cs.DS↗

arXiv2019

Parallel decompression of gzip-compressed files and random access to DNA sequences

Decompressing a file made by the gzip program at an arbitrary location is in principle impossible, due to the nature of the DEFLATE compression algorithm. Consequently, no existing program can take advantage of parallelism to rapidly decompress large gzip-compressed files. This is an unsatisfactory bottleneck, especially for the analysis of large sequencing data experiments. Here we propose a parallel algorithm and an implementation, pugz, that performs fast and exact decompression of any text file. We show that pugz is an order of magnitude faster than gunzip, and 5x faster than a highly-optimized sequential implementation (libdeflate). We also study the related problem of random access to compressed data. We give simple models and experimental results that shed light on the structure of gzip-compressed files containing DNA sequences. Preliminary results show that random access to sequences within a gzip-compressed FASTQ file is almost always feasible at low compression levels, yet is approximate at higher compression levels.

cs.DS↗

arXiv2018

Bipartite Graphs of Small Readability

We study a parameter of bipartite graphs called readability, introduced by Chikhi et al. (Discrete Applied Mathematics, 2016) and motivated by applications of overlap graphs in bioinformatics. The behavior of the parameter is poorly understood. The complexity of computing it is open and it is not known whether the decision version of the problem is in NP. The only known upper bound on the readability of a bipartite graph (following from a work of Braga and Meidanis, LATIN 2002) is exponential in the maximum degree of the graph. Graphs that arise in bioinformatic applications have low readability. In this paper, we focus on graph families with readability $o(n)$, where $n$ is the number of vertices. We show that the readability of $n$-vertex bipartite chain graphs is between $Ω(\log n)$ and $O(\sqrt{n})$. We give an efficiently testable characterization of bipartite graphs of readability at most $2$ and completely determine the readability of grids, showing in particular that their readability never exceeds $3$. As a consequence, we obtain a polynomial time algorithm to determine the readability of induced subgraphs of grids. One of the highlights of our techniques is the appearance of Euler's totient function in the analysis of the readability of bipartite chain graphs. We also develop a new technique for proving lower bounds on readability, which is applicable to dense graphs with a large number of distinct degrees.

cs.DM↗

arXiv2018

Dualities in Tree Representations

A characterization of the tree $T^*$ such that $\mathrm{BP}(T^*)=\overleftrightarrow{\mathrm{DFUDS}(T)}$, the reversal of $\mathrm{DFUDS}(T)$ is given. An immediate consequence is a rigorous characterization of the tree $\hat{T}$ such that $\mathrm{BP}(\hat{T})=\mathrm{DFUDS}(T)$. In summary, $\mathrm{BP}$ and $\mathrm{DFUDS}$ are unified within an encompassing framework, which might have the potential to imply future simplifications with regard to queries in $\mathrm{BP}$ and/or $\mathrm{DFUDS}$. Immediate benefits displayed here are to identify so far unnoted commonalities in most recent work on the Range Minimum Query problem, and to provide improvements for the Minimum Length Interval Query problem.

cs.DS↗

arXiv2018

Using Minimum Path Cover to Boost Dynamic Programming on DAGs: Co-Linear Chaining Extended

Aligning sequencing reads on graph representations of genomes is an important ingredient of pan-genomics. Such approaches typically find a set of local anchors that indicate plausible matches between substrings of a read to subpaths of the graph. These anchor matches are then combined to form a (semi-local) alignment of the complete read on a subpath. Co-linear chaining is an algorithmically rigorous approach to combine the anchors. It is a well-known approach for the case of two sequences as inputs. Here we extend the approach so that one of the inputs can be a directed acyclic graph (DAGs), e.g. a splicing graph in transcriptomics or a variant graph in pan-genomics. This extension to DAGs turns out to have a tight connection to the minimum path cover problem, asking for a minimum-cardinality set of paths that cover all the nodes of a DAG. We study the case when the size $k$ of a minimum path cover is small, which is often the case in practice. First, we propose an algorithm for finding a minimum path cover of a DAG $(V,E)$ in $O(k|E|\log|V|)$ time, improving all known time-bounds when $k$ is small and the DAG is not too dense. Second, we introduce a general technique for extending dynamic programming (DP) algorithms from sequences to DAGs. This is enabled by our minimum path cover algorithm, and works by mimicking the DP algorithm for sequences on each path of the minimum path cover. This technique generally produces algorithms that are slower than their counterparts on sequences only by a factor $k$. Our technique can be applied, for example, to the classical longest increasing subsequence and longest common subsequence problems, extended to labeled DAGs. Finally, we apply this technique to the co-linear chaining problem. We also implemented the new co-linear chaining approach. Experiments on splicing graphs show that the new method is efficient also in practice.

cs.DS↗

arXiv2017

Fast and scalable minimal perfect hashing for massive key sets

Minimal perfect hash functions provide space-efficient and collision-free hashing on static sets. Existing algorithms and implementations that build such functions have practical limitations on the number of input elements they can process, due to high construction time, RAM or external memory usage. We revisit a simple algorithm and show that it is highly competitive with the state of the art, especially in terms of construction time and memory usage. We provide a parallel C++ implementation called BBhash. It is capable of creating a minimal perfect hash function of $10^{10}$ elements in less than 7 minutes using 8 threads and 5 GB of memory, and the resulting function uses 3.7 bits/element. To the best of our knowledge, this is also the first implementation that has been successfully tested on an input of cardinality $10^{12}$. Source code: https://github.com/rizkg/BBHash

cs.DS↗

arXiv2015

On the readability of overlap digraphs

We introduce the graph parameter readability and study it as a function of the number of vertices in a graph. Given a digraph D, an injective overlap labeling assigns a unique string to each vertex such that there is an arc from x to y if and only if x properly overlaps y. The readability of D is the minimum string length for which an injective overlap labeling exists. In applications that utilize overlap digraphs (e.g., in bioinformatics), readability reflects the length of the strings from which the overlap digraph is constructed. We study the asymptotic behaviour of readability by casting it in purely graph theoretic terms (without any reference to strings). We prove upper and lower bounds on readability for certain graph families and general graphs

cs.DM↗

arXiv2014

On the representation of de Bruijn graphs

The de Bruijn graph plays an important role in bioinformatics, especially in the context of de novo assembly. However, the representation of the de Bruijn graph in memory is a computational bottleneck for many assemblers. Recent papers proposed a navigational data structure approach in order to improve memory usage. We prove several theoretical space lower bounds to show the limitation of these types of approaches. We further design and implement a general data structure (DBGFM) and demonstrate its use on a human whole-genome dataset, achieving space usage of 1.5 GB and a 46% improvement over previous approaches. As part of DBGFM, we develop the notion of frequency-based minimizers and show how it can be used to enumerate all maximal simple paths of the de Bruijn graph using only 43 MB of memory. Finally, we demonstrate that our approach can be integrated into an existing assembler by modifying the ABySS software to use DBGFM.

q-bio.QM↗

arXiv2013

Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

Background - The process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements, acceptance of newer read technologies) and in their final output (composition of assembled sequence). More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. The Assemblathon competitions are intended to assess current state-of-the-art methods in genome assembly. Results - In Assemblathon 2, we provided a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and snake). This resulted in a total of 43 submitted assemblies from 21 participating teams. We evaluated these assemblies using a combination of optical map data, Fosmid sequences, and several statistical methods. From over 100 different metrics, we chose ten key measures by which to assess the overall quality of the assemblies. Conclusions - Many current genome assemblers produced useful assemblies, containing a significant representation of their genes, regulatory sequences, and overall genome structure. However, the high degree of variability between the entries suggests that there is still much room for improvement in the field of genome assembly and that approaches which work well in assembling the genome of one species may not necessarily work well for another.

q-bio.GN↗

arXiv2013

Informed and Automated k-Mer Size Selection for Genome Assembly

Genome assembly tools based on the de Bruijn graph framework rely on a parameter k, which represents a trade-off between several competing effects that are difficult to quantify. There is currently a lack of tools that would automatically estimate the best k to use and/or quickly generate histograms of k-mer abundances that would allow the user to make an informed decision. We develop a fast and accurate sampling method that constructs approximate abundance histograms with a several orders of magnitude performance improvement over traditional methods. We then present a fast heuristic that uses the generated abundance histograms for putative k values to estimate the best possible value of k. We test the effectiveness of our tool using diverse sequencing datasets and find that its choice of k leads to some of the best assemblies. Our tool KmerGenie is freely available at: http://kmergenie.bx.psu.edu/

q-bio.GN↗