Biology subjects

Ochoa, I.

Publications and source records attributed to Ochoa, I..

23 records · Page 2Linked to original sources

bioRxiv2019

MassComp, a lossless compressor for mass spectrometry data

Background: Mass Spectrometry (MS) is a widely used technique in biology research, and has become key in proteomics and metabolomics analyses. As a result, the amount of MS data has significantly increased in recent years. For example, the MS repository MassIVE contains more than 123TB of data. Somehow surprisingly, these data are stored uncompressed, hence incurring a significant storage cost. Efficient representation of these data is therefore paramount to lessen the burden of storage and facilitate its dissemination. Results We present MassComp, a lossless compressor optimized for the numerical (m/z)-intensity pairs that account for most of the MS data. We tested MassComp on several MS data and show that it delivers on average a 46% reduction on the size of the numerical data, and up to 89%. These results correspond to an average improvement of more than 27% when compared to the general compressor gzip and of 40% when compared to the state-of-the-art numerical compressor FPC. When tested on entire files retrieved from the MassIVE repository, MassComp achieves on average a 59% size reduction. MassComp is written in C++ and freely available at https://github.com/iochoa/MassComp. Conclusions: The compression performance of MassComp demonstrates its potential to significantly reduce the footprint of MS data, and shows the benefits of designing specialized compression algorithms tailored to MS data. MassComp is an addition to the family of omics compression algorithms designed to lessen the storage burden and facilitate the exchange and dissemination of omics data.

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bioRxiv2019

VEF: a Variant Filtering tool based on Ensemble methods

MotivationVariant discovery is crucial in medical and clinical research, especially in the setting of personalized medicine. As such, precision in variant identification is paramount. However, variants identified by current genomic analysis pipelines contain many false positives (i.e., incorrectly called variants). These can be potentially eliminated by applying state-of-the-art filtering tools, such as the Variant Quality Score Recalibration (VQSR) or the Hard Filtering (HF), both proposed by GATK. However, these methods are very user-dependent and fail to run in some cases. We propose VEF, a variant filtering tool based on ensemble methods that overcomes the main drawbacks of VQSR and the HF. Contrary to these methods, we treat filtering as a supervised learning problem. This is possible by using for training variant call data for which the set of "true" variants is known, i.e., a gold standard exists. Hence, we can classify each variant in the training VCF file as true or false using the gold standard, and further use the annotations of each variant as features for the classification problem. Once trained, VEF can be directly applied to filter the variants contained in a given VCF file. Analysis of several ensemble methods revealed random forest as offering the best performance, and hence VEF uses a random forest for the classification task. ResultsAfter training VEF on a Whole Genome Sequencing (WGS) Human dataset of sample NA12878, we tested its performance on a WGS Human dataset of sample NA24385. For these two samples, the set of high-confident variants has been produced and made available. Results show that the proposed filtering tool VEF consistently outperforms VQSR and HF. In addition, we show that VEF generalizes well even when some features have missing values, and when the training and testing datasets differ either in coverage or in the sequencing machine that was used to generate the data. Finally, since the training needs to be performed only once, there is a significant saving in running time when compared to VQSR (50 minutes versus 4 minutes approximately for filtering the SNPs of WGS Human sample NA24385). Code and scripts available at: github.com/ChuanyiZ/vef.

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bioRxiv2019

E2M: A Deep Learning Framework for Associating Combinatorial Methylation Patterns with Gene Expression

We focus on the new problem of determining which methylation patterns in gene promoters strongly associate with gene expression in cancer cells of different types. Although a number of results regarding the influence of methylation on expression data have been reported in the literature, our approach is unique in so far that it retrospectively predicts the combinations of methylated sites in promoter regions of genes that are reflected in the expression data. Reversing the traditional prediction order in many cases makes estimation of the model parameters easier, as real-valued data are used to predict categorical data, rather than vice-versa; in addition, our approach allows one to better assess the overall influence of methylation in modulating expression via state-of-the-art learning methods. For this purpose, we developed a novel neural network learning framework termed E2M (Expression-to-Methylation) to predict the status of different methylation sites in promoter regions of several bio-marker genes based on a sufficient statistics of the whole gene expression captured through Landmark genes. We ran our experiments on unquantized and quantized expression sets and neural network weights to illustrate the robustness of the method and reduce the storage footprint of the processing pipeline.

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bioRxiv2018

An introduction to MPEG-G, the new ISO standard for genomic information representation

The MPEG-G standardization initiative is a coordinated international effort to specify a compressed data format that enables large scale genomic data to be processed, transported and shared. The standard consists of a set of specifications (i.e., a book) describing: i) a nor-mative format syntax, and ii) a normative decoding process to retrieve the information coded in a compliant file or bitstream. Such decoding process enables the use of leading-edge com-pression technologies that have exhibited significant compression gains over currently used formats for storage of unaligned and aligned sequencing reads. Additionally, the standard provides a wealth of much needed functionality, such as selective access, data aggregation, ap-plication programming interfaces to the compressed data, standard interfaces to support data protection mechanisms, support for streaming and a procedure to assess the conformance of implementations. ISO/IEC is engaged in supporting the maintenance and availability of the standard specification, which guarantees the perenniality of applications using MPEG-G. Fi-nally, the standard ensures interoperability and integration with existing genomic information processing pipelines by providing support for conversion from the FASTQ/SAM/BAM file formats.\n\nIn this paper we provide an overview of the MPEG-G specification, with particular focus on the main advantages and novel functionality it offers. As the standard only specifies the decoding process, encoding performance, both in terms of speed and compression ratio, can vary depending on specific encoder implementations, and will likely improve during the lifetime of MPEG-G. Hence, the performance statistics provided here are only indicative baseline examples of the technologies included in the standard.

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bioRxiv2017

FaStore - a space-saving solution for raw sequencing data

The affordability of DNA sequencing has led to the generation of unprecedented volumes of raw sequencing data. These data must be stored, processed, and transmitted, which poses significant challenges. To facilitate this effort, we introduce FaStore, a specialized compressor for FASTQ files. The proposed algorithm does not use any reference sequences for compression, and permits the user to choose from several lossy modes to improve the overall compression ratio, depending on the specific needs. We demonstrate through extensive simulations that FaStore achieves a significant improvement in compression ratio with respect to previously proposed algorithms for this task. In addition, we perform an analysis on the effect that the different lossy modes have on variant calling, the most widely used application for clinical decision making, especially important in the era of precision medicine. We show that lossy compression can offer significant compression gains, while preserving the essential genomic information and without affecting the variant calling performance.

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