BdBG: a bucket-based method for compressing genome sequencing data with dynamic de Bruijn graphs

Dramatic increases in data produced by next-generation sequencing (NGS) technologies demand data compression tools for saving storage space. However, effective and efficient data compression for genome sequencing data has remained an unresolved challenge in NGS data studies. In this paper, we propose a novel alignment-free and reference-free compression method, BdBG, which is the first to compress genome sequencing data with dynamic de Bruijn graphs based on the data after bucketing. Compared with existing de Bruijn graph methods, BdBG only stored a list of bucket indexes and bifurcations for the raw read sequences, and this feature can effectively reduce storage space. Experimental results on several genome sequencing datasets show the effectiveness of BdBG over three state-of-the-art methods. BdBG is written in python and it is an open source software distributed under the MIT license, available for download at https://github.com/rongjiewang/BdBG

Computational biology in the 21st century

Computational biologists answer biological and biomedical questions by using computation in support of—or in place of—laboratory procedures, hoping to obtain more accurate answers at a greatly reduced cost. The past two decades have seen unprecedented technological progress with regard to generating biological data; next-generation sequencing, mass spectrometry, microarrays, cryo-electron microscopy, and other highthroughput approaches have led to an explosion of data. However, this explosion is a mixed blessing. On the one hand, the scale and scope of data should allow new insights into genetic and infectious diseases, cancer, basic biology, and even human migration patterns. On the other hand, researchers are generating datasets so massive that it has become difficult to analyze them to discover patterns that give clues to the underlying biological processes.National Institutes of Health. (U.S.) ( grant GM108348)Hertz Foundatio

Lossy Compression of Quality Values in Sequencing Data

The dropping cost of sequencing human DNA has allowed for fast development of several projects around the world generating huge amounts of DNA sequencing data. This deluge of data has run up against limited storage space, a problem that researchers are trying to solve through compression techniques. In this study we address the compression of SAM files, the standard output files for DNA alignment. We specifically study lossy compression techniques used for quality values reported in the SAM file and analyze the impact of such lossy techniques on the CRAM format. We present a series of experiments using a data set corresponding to individual NA12878 with three different fold coverages. We introduce a new lossy model, dynamic binning, and compare its performance to other lossy techniques, namely Illumina binning, LEON and QVZ. We analyze the compression ratio when using CRAM and also study the impact of the lossy techniques on SNP calling. Our results show that lossy techniques allow a better CRAM compression ratio. Furthermore, we show that SNP calling performance is not negatively affected and may even be boosted.Natural Sciences and Engineering Research Council of Canad

Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph

International audienceData volumes generated by next-generation sequencing (NGS) technologies is now a major concern for both data storage and transmission. This triggered the need for more efficient methods than general purpose compression tools, such as the widely used gzip method.We present a novel reference-free method meant to compress data issued from high throughput sequencing technologies. Our approach, implemented in the software LEON, employs techniques derived from existing assembly principles. The method is based on a reference probabilistic de Bruijn Graph, built de novo from the set of reads and stored in a Bloom filter. Each read is encoded as a path in this graph, by memorizing an anchoring kmer and a list of bifurcations. The same probabilistic de Bruijn Graph is used to perform a lossy transformation of the quality scores, which allows to obtain higher compression rates without losing pertinent information for downstream analyses.LEON was run on various real sequencing datasets (whole genome, exome, RNA-seq or metagenomics). In all cases, LEON showed higher overall compression ratios than state-of-the-art compression software. On a C. elegans whole genome sequencing dataset, LEON divided the original file size by more than 20. LEON is an open source software, distributed under GNU affero GPL License, available for download at http://gatb.inria.fr/software/leon/

Hamming-shifting graph of genomic short reads: Efficient construction and its application for compression

Graphs such as de Bruijn graphs and OLC (overlap-layout-consensus) graphs have been widely adopted for the de novo assembly of genomic short reads. This work studies another important problem in the field: how graphs can be used for high-performance compression of the large-scale sequencing data. We present a novel graph definition named Hamming-Shifting graph to address this problem. The definition originates from the technological characteristics of next-generation sequencing machines, aiming to link all pairs of distinct reads that have a small Hamming distance or a small shifting offset or both. We compute multiple lexicographically minimal k-mers to index the reads for an efficient search of the weight-lightest edges, and we prove a very high probability of successfully detecting these edges. The resulted graph creates a full mutual reference of the reads to cascade a code-minimized transfer of every child-read for an optimal compression. We conducted compression experiments on the minimum spanning forest of this extremely sparse graph, and achieved a 10 − 30% more file size reduction compared to the best compression results using existing algorithms. As future work, the separation and connectivity degrees of these giant graphs can be used as economical measurements or protocols for quick quality assessment of wet-lab machines, for sufficiency control of genomic library preparation, and for accurate de novo genome assembly

Compression algorithms for biomedical signals and nanopore sequencing data

The massive generation of biological digital information creates various computing challenges such as its storage and transmission. For example, biomedical signals, such as electroencephalograms (EEG), are recorded by multiple sensors over long periods of time, resulting in large volumes of data. Another example is genome DNA sequencing data, where the amount of data generated globally is seeing explosive growth, leading to increasing needs for processing, storage, and transmission resources. In this thesis we investigate the use of data compression techniques for this problem, in two different scenarios where computational efficiency is crucial. First we study the compression of multi-channel biomedical signals. We present a new lossless data compressor for multi-channel signals, GSC, which achieves compression performance similar to the state of the art, while being more computationally efficient than other available alternatives. The compressor uses two novel integer-based implementations of the predictive coding and expert advice schemes for multi-channel signals. We also develop a version of GSC optimized for EEG data. This version manages to significantly lower compression times while attaining similar compression performance for that specic type of signal. In a second scenario we study the compression of DNA sequencing data produced by nanopore sequencing technologies. We present two novel lossless compression algorithms specifically tailored to nanopore FASTQ files. ENANO is a reference-free compressor, which mainly focuses on the compression of quality scores. It achieves state of the art compression performance, while being fast and with low memory consumption when compared to other popular FASTQ compression tools. On the other hand, RENANO is a reference-based compressor, which improves on ENANO, by providing a more efficient base call sequence compression component. For RENANO two algorithms are introduced, corresponding to the following scenarios: a reference genome is available without cost to both the compressor and the decompressor; and the reference genome is available only on the compressor side, and a compacted version of the reference is included in the compressed le. Both algorithms of RENANO significantly improve the compression performance of ENANO, with similar compression times, and higher memory requirements.La generación masiva de información digital biológica da lugar a múltiples desafíos informáticos, como su almacenamiento y transmisión. Por ejemplo, las señales biomédicas, como los electroencefalogramas (EEG), son generadas por múltiples sensores registrando medidas en simultaneo durante largos períodos de tiempo, generando grandes volúmenes de datos. Otro ejemplo son los datos de secuenciación de ADN, en donde la cantidad de datos a nivel mundial esta creciendo de forma explosiva, lo que da lugar a una gran necesidad de recursos de procesamiento, almacenamiento y transmisión. En esta tesis investigamos como aplicar técnicas de compresión de datos para atacar este problema, en dos escenarios diferentes donde la eficiencia computacional juega un rol importante. Primero estudiamos la compresión de señales biomédicas multicanal. Comenzamos presentando un nuevo compresor de datos sin perdida para señales multicanal, GSC, que logra obtener niveles de compresión en el estado del arte y que al mismo tiempo es mas eficiente computacionalmente que otras alternativas disponibles. El compresor utiliza dos nuevas implementaciones de los esquemas de codificación predictiva y de asesoramiento de expertos para señales multicanal, basadas en aritmética de enteros. También presentamos una versión de GSC optimizada para datos de EEG. Esta versión logra reducir significativamente los tiempos de compresión, sin deteriorar significativamente los niveles de compresión para datos de EEG. En un segundo escenario estudiamos la compresión de datos de secuenciación de ADN generados por tecnologías de secuenciación por nanoporos. En este sentido, presentamos dos nuevos algoritmos de compresión sin perdida, específicamente diseñados para archivos FASTQ generados por tecnología de nanoporos. ENANO es un compresor libre de referencia, enfocado principalmente en la compresión de los valores de calidad de las bases. ENANO alcanza niveles de compresión en el estado del arte, siendo a la vez mas eficiente computacionalmente que otras herramientas populares de compresión de archivos FASTQ. Por otro lado, RENANO es un compresor basado en la utilización de una referencia, que mejora el rendimiento de ENANO, a partir de un nuevo esquema de compresión de las secuencias de bases. Presentamos dos variantes de RENANO, correspondientes a los siguientes escenarios: (i) se tiene a disposición un genoma de referencia, tanto del lado del compresor como del descompresor, y (ii) se tiene un genoma de referencia disponible solo del lado del compresor, y se incluye una versión compacta de la referencia en el archivo comprimido. Ambas variantes de RENANO mejoran significativamente los niveles compresión de ENANO, alcanzando tiempos de compresión similares y un mayor consumo de memoria

Computational problems of analysis of short next generation sequencing reads

Short read next generation sequencing (NGS) has significant impacts on modern genomics, genetics, cell biology and medicine, especially on meta-genomics, comparative genomics, polymorphism detection, mutation screening, transcriptome profiling, methylation profiling, chromatin remodelling and many more applications. However, NGS are prone for errors which complicate scientific conclusions. NGS technologies consist of shearing DNA molecules into collection of numerous small fragments, called a ‘library’, and their further extensive parallel sequencing. These sequenced overlapping fragments are called ‘reads’, they are assembled into contiguous strings. The contiguous sequences are in turn assembled into genomes for further analysis. Computational sequencing problems are those arising from numerical processing of sequenced samples. The numerical processing involves procedures such as: quality-scoring, mapping/assembling, and surprisingly, error-correction of a data. This paper is reviewing post-processing errors and computational methods to discern them. It also includes sequencing dictionary. We present here quality control of raw data, errors arising at the steps of alignment of sequencing reads to a reference genome and assembly. Finally this work presents identification of mutations (“Variant calling”) in sequencing data and its quality control

Compression of DNA sequencing data

With the release of the latest generations of sequencing machines, the cost of sequencing a whole human genome has dropped to less than US$1,000. The potential applications in several fields lead to the forecast that the amount of DNA sequencing data will soon surpass the volume of other types of data, such as video data. In this dissertation, we present novel data compression technologies with the aim of enhancing storage, transmission, and processing of DNA sequencing data. The first contribution in this dissertation is a method for the compression of aligned reads, i.e., read-out sequence fragments that have been aligned to a reference sequence. The method improves compression by implicitly assembling local parts of the underlying sequences. Compared to the state of the art, our method achieves the best trade-off between memory usage and compressed size. Our second contribution is a method for the quantization and compression of quality scores, i.e., values that quantify the error probability of each read-out base. Specifically, we propose two Bayesian models that are used to precisely control the quantization. With our method it is possible to compress the data down to 0.15 bit per quality score. Notably, we can recommend a particular parametrization for one of our models which—by removing noise from the data as a side effect—does not lead to any degradation in the distortion metric. This parametrization achieves an average rate of 0.45 bit per quality score. The third contribution is the first implementation of an entropy codec compliant to MPEG-G. We show that, compared to the state of the art, our method achieves the best compression ranks on average, and that adding our method to CRAM would be beneficial both in terms of achievable compression and speed. Finally, we provide an overview of the standardization landscape, and in particular of MPEG-G, in which our contributions have been integrated.Mit der Einführung der neuesten Generationen von Sequenziermaschinen sind die Kosten für die Sequenzierung eines menschlichen Genoms auf weniger als 1.000 US-Dollar gesunken. Es wird prognostiziert, dass die Menge der Sequenzierungsdaten bald diejenige anderer Datentypen, wie z.B. Videodaten, übersteigen wird. Daher werden in dieser Arbeit neue Datenkompressionsverfahren zur Verbesserung der Speicherung, Übertragung und Verarbeitung von Sequenzierungsdaten vorgestellt. Der erste Beitrag in dieser Arbeit ist eine Methode zur Komprimierung von alignierten Reads, d.h. ausgelesenen Sequenzfragmenten, die an eine Referenzsequenz angeglichen wurden. Die Methode verbessert die Komprimierung, indem sie die Reads nutzt, um implizit lokale Teile der zugrunde liegenden Sequenzen zu schätzen. Im Vergleich zum Stand der Technik erzielt die Methode das beste Ergebnis in einer gemeinsamen Betrachtung von Speichernutzung und erzielter Komprimierung. Der zweite Beitrag ist eine Methode zur Quantisierung und Komprimierung von Qualitätswerten, welche die Fehlerwahrscheinlichkeit jeder ausgelesenen Base quantifizieren. Konkret werden zwei Bayes’sche Modelle vorgeschlagen, mit denen die Quantisierung präzise gesteuert werden kann. Mit der vorgeschlagenen Methode können die Daten auf bis zu 0,15 Bit pro Qualitätswert komprimiert werden. Besonders hervorzuheben ist, dass eine bestimmte Parametrisierung für eines der Modelle empfohlen werden kann, die – durch die Entfernung von Rauschen aus den Daten als Nebeneffekt – zu keiner Verschlechterung der Verzerrungsmetrik führt. Mit dieser Parametrisierung wird eine durchschnittliche Rate von 0,45 Bit pro Qualitätswert erreicht. Der dritte Beitrag ist die erste Implementierung eines MPEG-G-konformen Entropie-Codecs. Es wird gezeigt, dass der vorgeschlagene Codec die durchschnittlich besten Kompressionswerte im Vergleich zum Stand der Technik erzielt und dass die Aufnahme des Codecs in CRAM sowohl hinsichtlich der erreichbaren Kompression als auch der Geschwindigkeit von Vorteil wäre. Abschließend wird ein Überblick über Standards zur Komprimierung von Sequenzierungsdaten gegeben. Insbesondere wird hier auf MPEG-G eingangen, da alle Beiträge dieser Arbeit in MPEG-G integriert wurden

FPGA acceleration of DNA sequence alignment: design analysis and optimization

Existing FPGA accelerators for short read mapping often fail to utilize the complete biological information in sequencing data for simple hardware design, leading to missed or incorrect alignment. In this work, we propose a runtime reconfigurable alignment pipeline that considers all information in sequencing data for the biologically accurate acceleration of short read mapping. We focus our efforts on accelerating two string matching techniques: FM-index and the Smith-Waterman algorithm with the affine-gap model which are commonly used in short read mapping. We further optimize the FPGA hardware using a design analyzer and merger to improve alignment performance. The contributions of this work are as follows. 1. We accelerate the exact-match and mismatch alignment by leveraging the FM-index technique. We optimize memory access by compressing the data structure and interleaving the access with multiple short reads. The FM-index hardware also considers complete information in the read data to maximize accuracy. 2. We propose a seed-and-extend model to accelerate alignment with indels. The FM-index hardware is extended to support the seeding stage while a Smith-Waterman implementation with the affine-gap model is developed on FPGA for the extension stage. This model can improve the efficiency of indel alignment with comparable accuracy versus state-of-the-art software. 3. We present an approach for merging multiple FPGA designs into a single hardware design, so that multiple place-and-route tasks can be replaced by a single task to speed up functional evaluation of designs. We first experiment with this approach to demonstrate its feasibility for different designs. Then we apply this approach to optimize one of the proposed FPGA aligners for better alignment performance.Open Acces