Video Tutorial: Serghei Mangul’s Introduction to UNIX Workshops

We present three video recordings of workshops that ZarLab postdoctoral scholar Serghei Mangul developed under the UCLA Institute for Quantitative and Computational Biosciences Collaboratory and delivered to Bruins-In-Genomics (B.I.G.) SUMMER participants. B.I.G. SUMMER is an intensive, practical experience in genomics and bioinformatics for undergraduate students who are interested in integrating quantitative and biological knowledge and considering pursuing graduate degrees in the biological, biomedical, or health sciences.

An important question for undergraduates considering careers in the biosciences is whether or not biologists need to develop robust programming skills. Biology students without backgrounds in computer science are often intimidated by applications that require inputting code or negotiating systems that lack a graphical interface, such as Unix, R, SASS, and Python.


“Becoming a programmer” may seem daunting to many students in biology, but an ability to analyze sequencing data represents a competitive advantage in today’s age of big data and next generation sequencing. By gaining familiarity with Unix, these students may find it easier to engage with other applications and programming languages commonly used in computational biology. In order to use Unix effectively, students must learn how to directly enter functional commands line-by-line into a workbench that manages multiple platforms and a unified filesystem—without the familiar aid of a graphical interface.

In this three-part series of workshops, Dr. Mangul provides just enough information for students with no computational background to get started using Unix for analytical tasks. These workshops aim to help participants learn key commands and develop fundamental skills, such as connecting, writing, and submitting basic shell scripts to a cluster.

Slides and more information about the workshop are available at the following webpage:

Introduction to UNIX 1/3

Introduction to UNIX 2/3

Introduction to UNIX 3/3


Thesis Defense: Dr. Farhad Hormozdiari

Farhad Hormozdiari successfully defended his thesis,”Statistical Methods to Understand the Genetic Architecture of Complex Traits,” on Tuesday, May 17, 2016 in Boelter 4760. His talk, which is posted on our YouTube channel ZarlabUCLA, discusses methods for applying CAVIAR to understand the underlying mechanism of GWAS risk loci, introduces eCAVIAR, a statistical method capable of computing the probability that the same variant is responsible for both the GWAS and eQTL signal, while accounting for complex LD structure, and proposes an approach called phenotype imputation that allows GWAS computation on a phenotype that is difficult to collect.




More details about Farhad’s research are available in the following papers:

Hormozdiari, Farhad; Kichaev, Gleb; Yang, Wen-Yun Y; Pasaniuc, Bogdan; Eskin, Eleazar

Identification of causal genes for complex traits. Journal Article

In: Bioinformatics, 31 (12), pp. i206-i213, 2015, ISSN: 1367-4811.

Abstract | Links | BibTeX

Hormozdiari, Farhad; Joo, Jong Wha J; Wadia, Akshay ; Guan, Feng ; Ostrosky, Rafail ; Sahai, Amit ; Eskin, Eleazar

Privacy preserving protocol for detecting genetic relatives using rare variants. Journal Article

In: Bioinformatics, 30 (12), pp. i204-i211, 2014, ISSN: 1367-4811.

Abstract | Links | BibTeX

Hormozdiari, Farhad; Kostem, Emrah ; Kang, Eun Yong ; Pasaniuc, Bogdan ; Eskin, Eleazar

Identifying causal variants at Loci with multiple signals of association. Journal Article

In: Genetics, 198 (2), pp. 497-508, 2014, ISSN: 1943-2631.

Abstract | Links | BibTeX

Eskin, Itamar; Hormozdiari, Farhad; Conde, Lucia; Riby, Jacques; Skibola, Chris; Eskin, Eleazar; Halperin, Eran

eALPS: Estimating Abundance Levels in Pooled Sequencing Using Available Genotyping Data. Journal Article

In: J Comput Biol, 2013, ISSN: 1557-8666.

Abstract | Links | BibTeX

Thesis Defense: Dr. Jong Wha (Joanne) Joo

Jong Wha (Joanne) Joo successfully defended her thesis,”Design of efficient and accurate statistical approaches to correct for confounding effects in genetic association studies,” on Friday, December 4, 2015 in Boelter 4760.  Her talk, which is posted on our YouTube channel ZarlabUCLA, discusses using a mixed model analysis (GAMMA) to efficiently analyzes large numbers of phenotypes while simultaneously considering population structure, an expression quantitative trait loci (eQTL) mapping tool to eliminate spurious hotspots while retaining genuine regulatory hotspots, and a multiple testing correction method (slideLMM) for linear mixed models.
More details about her research are available in the three papers she discusses:

Joo, Jong Wha J; Hormozdiari, Farhad; Han, Buhm; Eskin, Eleazar

Multiple testing correction in linear mixed models. Journal Article

In: Genome Biol, 17 (1), pp. 62, 2016, ISSN: 1474-760X.

Abstract | Links | BibTeX

Joo, Jong Wha J; Kang, Eun Yong; Org, Elin; Furlotte, Nick; Parks, Brian; Lusis, Aldons J; Eskin, Eleazar

Efficient and Accurate Multiple-Phenotypes Regression Method for High Dimensional Data Considering Population Structure Book Chapter

In: Research in Computational Molecular Biology, pp. 136-153, Springer International Publishing, 2015.

Abstract | Links | BibTeX

Joo, Jong Wha J; Sul, Jae Hoon ; Han, Buhm ; Ye, Chun ; Eskin, Eleazar

Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies. Journal Article

In: Genome Biol, 15 (4), pp. R61, 2014, ISSN: 1465-6914.

Abstract | Links | BibTeX