Bi-Dimensional Binning for Big Genomic Datasets

Binning the genome is used in order to parallelize big data operations upon regions. In this extended abstract, we comparatively evaluate the performance and scalability of Spark and SciDB implementations over datasets consisting of billions of genomic regions. In particular, we introduce an original method for binning the genome, i.e. partitioning it into sections of small sizes, and show that it outperforms conventional binning used by SciDB and closes the gap between SciDB and a Spark-based implementation. The concept of bi-dimensional binning is new and can be extended to other systems and technologies

DNABERT-S: Learning Species-Aware DNA Embedding with Genome Foundation Models

Effective DNA embedding remains crucial in genomic analysis, particularly in scenarios lacking labeled data for model fine-tuning, despite the significant advancements in genome foundation models. A prime example is metagenomics binning, a critical process in microbiome research that aims to group DNA sequences by their species from a complex mixture of DNA sequences derived from potentially thousands of distinct, often uncharacterized species. To fill the lack of effective DNA embedding models, we introduce DNABERT-S, a genome foundation model that specializes in creating species-aware DNA embeddings. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C$^2$LR) strategy. Empirical results on 18 diverse datasets showed DNABERT-S's remarkable performance. It outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training while doubling the Adjusted Rand Index (ARI) in species clustering and substantially increasing the number of correctly identified species in metagenomics binning. The code, data, and pre-trained model are publicly available at https://github.com/Zhihan1996/DNABERT_S

Conceptual models and databases for searching the genome

Genomics is an extremely complex domain, in terms of concepts, their relations, and their representations in data. This tutorial introduces the use of ER models in the context of genomic systems: conceptual models are of great help for simplifying this domain and making it actionable. We carry out a review of successful models presented in the literature for representing biologically relevant entities and grounding them in databases. We draw a difference between conceptual models that aim to explain the domain and conceptual models that aim to support database design and heterogeneous data integration. Genomic experiments and/or sequences are described by several metadata, specifying information on the sampled organism, the used technology, and the organizational process behind the experiment. Instead, we call data the actual regions of the genome that have been read by sequencing technologies and encoded into a machiner readable representation. First, we show how data and metadata can be modeled, then we exploit the proposed models for designing search systems, visualizers, and analysis environments. Both domains of human genomics and viral genomics are addressed, surveying several use cases and applications of broader public interest. The tutorial is relevant to the EDBT community because it demonstrates the usefulness of conceptual models’ principles within very current domains; in addition, it offers a concrete example of conceptual models’ use, setting the premises for interdisciplinary collaboration with a greater public (possibly including life science researchers)

Long-term impact of fecal transplantation in healthy volunteers

Fecal microbiota transplantation (FMT) has been recently approved by FDA for the treatment of refractory recurrent clostridial colitis (rCDI). Success of FTM in treatment of rCDI led to a number of studies investigating the effectiveness of its application in the other gastrointestinal diseases. However, in the majority of studies the effects of FMT were evaluated on the patients with initially altered microbiota. The aim of our study was to estimate effects of FMT on the gut microbiota composition in healthy volunteers and to monitor its long-term outcomes.Peer ReviewedPostprint (published version

Big tranSMART for clinical decision making

Molecular profiling data based patient stratification plays a key role in clinical decision making, such as identification of disease subgroups and prediction of treatment responses of individual subjects. Many existing knowledge management systems like tranSMART enable scientists to do such analysis. But in the big data era, molecular profiling data size increases sharply due to new biological techniques, such as next generation sequencing. None of the existing storage systems work well while considering the three ”V” features of big data (Volume, Variety, and Velocity). New Key Value data stores like Apache HBase and Google Bigtable can provide high speed queries by the Key. These databases can be modeled as Distributed Ordered Table (DOT), which horizontally partitions a table into regions and distributes regions to region servers by the Key. However, none of existing data models work well for DOT. A Collaborative Genomic Data Model (CGDM) has been designed to solve all these is- sues. CGDM creates three Collaborative Global Clustering Index Tables to improve the data query velocity. Microarray implementation of CGDM on HBase performed up to 246, 7 and 20 times faster than the relational data model on HBase, MySQL Cluster and MongoDB. Single nucleotide polymorphism implementation of CGDM on HBase outperformed the relational model on HBase and MySQL Cluster by up to 351 and 9 times. Raw sequence implementation of CGDM on HBase gains up to 440-fold and 22-fold speedup, compared to the sequence alignment map format implemented in HBase and a binary alignment map server. The integration into tranSMART shows up to 7-fold speedup in the data export function. In addition, a popular hierarchical clustering algorithm in tranSMART has been used as an application to indicate how CGDM can influence the velocity of the algorithm. The optimized method using CGDM performs more than 7 times faster than the same method using the relational model implemented in MySQL Cluster.Open Acces

A Robust Unified Graph Model Based on Molecular Data Binning for Subtype Discovery in High-dimensional Spaces

Machine learning (ML) is a subfield of artificial intelligence (AI) that has already revolutionised the world around us. It is a widely employed process for discovering patterns and groups within datasets. It has a wide range of applications including disease subtyping, which aims to discover intrinsic subtypes of disease in large-scale unlabelled data. Whilst the groups discovered in multi-view high-dimensional data by ML algorithms are promising, their capacity to identify pertinent and meaningful groups is limited by the presence of data variability and outliers. Since outlier values represent potential but unlikely outcomes, they are statistically and philosophically fascinating. Therefore, the primary aim of this thesis was to propose a robust approach that discovers meaningful groups while considering the presence of data variability and outliers in the data. To achieve this aim, a novel robust approach (ROMDEX) was developed that utilised the proposed intermediate graph models (IMGs) for robust computation of proximity between observations in the data. Finally, a robust multi-view graph-based clustering approach was developed based on ROMDEX that improved the discovery of meaningful groups that were hidden behind the noise in the data. The proposed approach was validated on real-world, and synthetic data for disease subtyping. Additionally, the stability of the approach was assessed by evaluating its performance across different levels of noise in clustering data. The results were evaluated through Kaplan-Meier survival time analysis for disease subtyping. Also, the concordance index (CI) and normalised mutual information (NMI) are used to evaluate the predictive ability of the proposed clustering model. Additionally, the accuracy, Kappa statistic and rand index are computed to evaluate the clustering stability against various levels of Gaussian noise. The proposed approach outperformed the existing state-of-the-art approaches MRGC, PINS, SNF, Consensus Clustering, and Icluster+ on these datasets. The findings for all datasets were outstanding, demonstrating the predictive ability of the proposed unsupervised graph-based clustering approach

A Statistical Analysis and Machine Learning of Genomic Data

Machine learning enables a computer to learn a relationship between two assumingly related types of information. One type of information could thus be used to predict any lack of informaion in the other using the learned relationship. During the last decades, it has become cheaper to collect biological information, which has resulted in increasingly large amounts of data. Biological information such as DNA is currently analyzed by a variety of tools. Although machine learning has already been used in various projects, a flexible tool for analyzing generic biological challenges has not yet been made. The recent advancements in the DNA sequencing technologies (nextgeneration sequencing) decreased the time of sequencing a human genome from weeks to hours and the cost of sequencing a human genome from million dollars to a thousand dollars. Due to this drop in costs, a large amount of genomic data are produced. This thesis implemented the supervised and unsupervised machine learning algorithms for the genomic data. Distances are an integral part of all machine learning algorithms and hence play a central role in the analysis of most genomic data. The distance that is used for any particular task can have a profound effect on the output of the machine learning method and thus, it is essential that users ensure that the same distance method is used when comparing machine learning algorithms

A bioinformatics potpourri

© 2018 The Author(s). The 16th International Conference on Bioinformatics (InCoB) was held at Tsinghua University, Shenzhen from September 20 to 22, 2017. The annual conference of the Asia-Pacific Bioinformatics Network featured six keynotes, two invited talks, a panel discussion on big data driven bioinformatics and precision medicine, and 66 oral presentations of accepted research articles or posters. Fifty-seven articles comprising a topic assortment of algorithms, biomolecular networks, cancer and disease informatics, drug-target interactions and drug efficacy, gene regulation and expression, imaging, immunoinformatics, metagenomics, next generation sequencing for genomics and transcriptomics, ontologies, post-translational modification, and structural bioinformatics are the subject of this editorial for the InCoB2017 supplement issues in BMC Genomics, BMC Bioinformatics, BMC Systems Biology and BMC Medical Genomics. New Delhi will be the location of InCoB2018, scheduled for September 26-28, 2018