Tag: sergheimangul

Involving undergraduates in genomics research to narrow the education-research gap

Posted on by

Serghei Mangul and Lana Martin, together with Eleazar Eskin, recently wrote a paper describing a model for training undergraduates in Bioinformatics. Our paper is available online as a preprint and is under review at a peer-reviewed journal.

The Education-Research Gap in Universities.

While the benefits of undergraduate research experiences (UREs) are recognized for undergraduates, the advantages of UREs for graduate students, post-doctoral scholars, and faculty are not clearly outlined.

Based on our experience mentoring undergraduates in ZarLab, we believe that the analysis of genomic data is particularly well-suited for successful involvement of undergraduates. In computational genomics research, undergraduate trainees who master a particular skill can contribute sufficient work to gain authorship on a peer-reviewed paper.

In our paper, we offer a framework for engaging undergraduates in genomics research while simultaneously improving lab productivity: first, identify particular “low-level” tasks that may take up to a week for an undergraduate to complete. Second, encourage students to “outsource” foundational education needs with workshops, online resources, and review articles. Third, genomics research labs can take advantage of department- and campus-wide undergraduate research and training initiatives.

The proposed strategy can be easily reproduced at other institutions, is pedagogically flexible, and is scalable from smaller to larger laboratory sizes. We hope that genomics researchers will involve undergraduates in more computational tasks that benefit both students and senior laboratory members.

Preprint copies of our manuscript are available for download here: https://peerj.com/preprints/3149/

In tandem with this paper, we created an online catalogue of resources and papers aimed at bridging the research-teaching divide in computational genomics: https://smangul1.github.io/undergraduates-in-genomics/

The full citation of our paper:
Mangul, S., Martin, L. and Eskin, E., 2017. Involving undergraduates in genomics research to narrow the education-research gap. PeerJ Preprints, 5, p.e3149v1.

 

Benefits of UREs to Research Lab and Undergraduates.

Addressing the Digital Divide in Contemporary Biology: Lessons from Teaching UNIX

Posted on by

Serghei Mangul and Lana Martin, together with Alexander Hoffmann, Matteo Pellegrini, and Eleazar Eskin, recently published a paper describing a workshop model for training scientists, who have no computer science background, to use UNIX. Our paper is available online as a preprint and will appear in an upcoming “Scientific Life” section of Trends in Biotechnology.

Scientists who are not trained in computer science face an enormous challenge analyzing high-throughput data. Serghei developed a series of workshops in response to growing demand for life and medical science researchers to analyze their own data using the command line.

Administered by UCLA’s Institute for Quantitative and Computational Biosciences (QCBio), these workshops are designed to help life and medical science researchers use applications that lack a graphical interface. Our paper presents a training model for these workshops—a flexible approach that can be implemented at any institution to teach use of command-line tools when the learner has little to no prior knowledge of UNIX.

QCBio currently offers similar workshops to the UCLA community. In tandem with this publication, we created an online catalogue of resources and papers aimed to provide first-time learners with basic knowledge of command line: https://smangul1.github.io/command-line-teaching/.

We encourage fellow instructors of Bioinformatics, as well as scientists who are new learners of the command line, to read our paper and share their thoughts! Email us at: lana [dot] martin [at] ucla [dot] edu.

 

The full citation of our paper:
Mangul, Serghei, Martin, Lana S., Hoffmann, Alexander, Pellegrini, Matteo, and Eskin, Eleazar. Addressing the Digital Divide in Contemporary Biology: Lessons from Teaching UNIX. Trends in Biotechnology; doi: 10.1016/j.tibtech.2017.06.007.

Advance preprint copies of our paper may be downloaded here: http://www.cell.com/trends/biotechnology/fulltext/S0167-7799(17)30156-7

Reference-free comparison of microbial communities via de Bruijn graphs

Posted on by

Microbial communities inhabiting the human body exhibit significant variability across different individuals and tissues and are likely play an important role in health and disease. Serghei Mangul and David Koslicki (Oregon State University) recently published a paper presenting a novel approach for characterizing microbial communities in metatranscriptomics studies. Koslicki developed this tool, which may help scientists explore the role microbiota play in disease development, especially when comparing microbiomes of healthy and disease subjects.

Identifying and characterizing the relative abundance of microbiota in different tissues is essential to better understanding the role of microbial communities in human health. Current approaches use reference databases to identify, classify, and compare microbial communities present in the individual host. However, existing databases are incomplete and rely on a limited compendium of reference genomes. Current reference-based approaches are unable to accurately determine microbial compositions to the extent that could be possible given the high resolution of data produced by today’s high throughput sequencing technology.

Framework of the study. For more information, download our paper.

Ideally, comparison of microbial communities across samples could circumvent this limiting classification step. Mangul and Koslicki recently developed EMDeBruijn, a reference-free approach that uses all available non-host microbial reads, not just those classified in reference databases, to compare microbial communities.

First, EMDeBruijn translates sequencing data to a de Bruijn graph, which represents overlaps between symbols in sequences. De Bruijn graphs are commonly used in de novo assembly of short read sequences to a genome, but have not yet been applied in a reference-free approach. EMDeBruijn then uses properties of the de Bruijn graphs to compare microbiome composition across individuals. This metric is reduced using the Earth Mover’s Distance (EMD), a statistic that can measure the distance between two probability distributions over a region.

In their recent paper, Mangul and Koslicki applied EMDeBruijn to study the composition and abundance levels of the microbial communities present in blood samples from coronary artery calcification (CAC) patients and controls. EMDeBruijn uses candidate microbial reads to differentiate between case (CAC-affected) and control (healthy) samples, and a filtered set of non-host reads are used to determine the composition of the blood microbiome. Hierarchical clustering using the EMDeBruijn metric successfully identifies several large clusters unique to samples from either health or control groups.

This study indicates the presence of the disease-specific microbial community structure in CAC patients, and points to the need for additional investigation of potentially causal relationships between the microbiome and CAC disease.

Using the same data set, Mangul and Koslicki compare the results of EMDeBruijn with those of current approaches. Existing computational methods, including MetaPhlAn and RDP’s NBC, discovered various microbial communities across the health and control samples. However, neither of these methods were able to identify any disease-specific patterns in the microbiome nor discriminate the samples into disease and healthy groups.

EMDeBruijn provides a powerful, species independent way to assess microbial diversity across individuals and subjects. For more information, see our paper, which was published in the Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics: http://dl.acm.org/citation.cfm?id=2975174.

Code implementing this method is available at: https://github.com/dkoslicki/EMDeBruijn.

Visualization of the EMDeBruijn Distance. a) Pictorial representation of 2-mer frequencies for two hypothetical samples, S1 and S2. b) The 2-mer frequencies overlaid the de Bruijn graph B2(A ). c) Representation of the flow used to compute EMD2(S1; S2); dark arrows denote mass moved from the initial node to the terminal node. d) Result of applying the flow to the 2-mer frequencies of S1.

This project was a collaboration that started at the Mathematical and Computational Approaches in High-Throughput Genomics program held in Fall 2011 at the Institute of Pure and Applied Mathematics (IPAM). Our on-going Computational Genomics Summer Institute (CGSI; also co-organized by IPAM) was inspired by the 2011 program. Check out the 2017 CGSI website for a preview of this summer’s programs – the deadline for applications is February 1, 2017!

The full citation to our paper is:

Mangul S, Koslicki D. Reference-free comparison of microbial communities via de Bruijn graphs. In Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. 2016 Oct 2 (pp. 68-77). Association for Computing Machinery, New York.