Steven Brenner, Professor

Open (1) Global human gene expression regulation by nonsense-mediated mRNA decay (computational biology).

Open. Apprentices needed for the fall semester. Please do NOT contact faculty before September 11th (the start of the 4th week of classes)! Enter your application on the web beginning August 16th. The deadline to apply is Tuesday, August 29th at 8 AM.

Gene expression in eukaryotes is regulated at several stages including initialization of mRNA transcription, post-translational modification, and nonsense-mediated mRNA decay (NMD). NMD is found to affect hundreds to thousands of human genes and its regulatory role in human tissues is largely unexplored.

We are now uncovering a high-confidence set of NMD targets by studying the transcriptome with key NMD splicing factors knocked down. The goal of this project is to develop a computational method to estimate the overall efficiency of NMD based on this confident set of NMD targets, apply it to human transcriptomes (both normal tissues and diseases), and develop a web tool.

Thus, the project will involve analyses of RNA-seq data, basic programming with perl/python, R language, and web development. After enrolling, the applicant will be trained in bioinformatics skills.


Day-to-day supervisor for this project: Zhiqiang Hu, Post-Doc

Qualifications: The student should have a keen interest in bioinformatics and should be familiar with at least one programming language. Applicants with a GPA less than 3.6 will be considered only in exceptional circumstances. The student must be able to attend 3-hour lab meeting every week, attend a subgroup meeting every week, adhere to other lab policy (including weekly notebooks to track research, semester reports) and register for credits, regardless of their program-specific requirements. Our lab is interdisciplinary, and we want to get a holistic picture of your background, so please include your unofficial transcript with your application.

Weekly Hours: 9-12 hrs

Related website: http://compbio.berkeley.edu

Open (2) Analysis of human whole exome sequencing data to identify causative variants in newborn screening for primary immunodeficiencies (computational biology)

Open. Apprentices needed for the fall semester. Please do NOT contact faculty before September 11th (the start of the 4th week of classes)! Enter your application on the web beginning August 16th. The deadline to apply is Tuesday, August 29th at 8 AM.

Genome sequencing technologies are beginning to assist clinicians in the detection and diagnosis of human diseases as part of the larger personalized medicine paradigm. In our lab, we analyze genomic data from newborns screened for severe combined immunodeficiency (SCID), a rare but life-threatening disorder in which infants appear healthy at birth, yet lack the adaptive immunity provided by T and B lymphocytes. SCID screening may be inconclusive, leaving affected newborns without a proper diagnosis or treatment.

This project involves analyzing whole genome sequence data from these newborns to identify the causative genetic mutations and precisely diagnose their disorders. Starting from millions of distinct mutations found in an individual’s DNA, the challenge is to develop methods to identify the gene and mutations that can explain the newborn’s clinical features. Strategies exist for narrowing the list using information such as the mutations’ rarity, effect on protein function, inheritance pattern, and predicted pathogenicity. However, an accurate, generalizable, and automated framework for prioritizing genes and mutations for a given phenotype is an active area of research. The undergraduate researcher will work with existing members in the Brenner Lab to implement and refine a generalizable framework for diagnosis of newborn immunodeficiencies using genomic data.


Day-to-day supervisor for this project: Andrew Sharo, Ph.D. candidate

Qualifications: The ideal candidate should have a background preferably in computational biology, genetics, computer science, molecular biology, biophysics, or a related field and an interest in solving biological problems with computers. The apprentice will be trained by postdocs and graduate students. Applicants with a GPA less than 3.6 will be considered only in exceptional circumstances. Other requirements: Candidates must: •Attend 3-hour lab meeting every week •Attend personal genomics subgroup meeting every week •Adhere to all lab policies (including weekly notebooks to track research, semester reports) •Must register for credits, regardless of program-specific requirements. Our lab is interdisciplinary, and we want to get a holistic picture of your background, so please include your unofficial transcript with your application.

Weekly Hours: 9-12 hrs

Related website: http://compbio.berkeley.edu

Open (3) Automatic identification of protein domains

Open. Apprentices needed for the fall semester. Please do NOT contact faculty before September 11th (the start of the 4th week of classes)! Enter your application on the web beginning August 16th. The deadline to apply is Tuesday, August 29th at 8 AM.

Proteins often fold into compact structural units, called domains. Protein domains are basic units of protein function and evolution. Delineating domain boundaries is a prerequisite for further analyses of protein structures. However, this process is largely a manual process and the accuracy of these computer programs is still not satisfactory. This project will include two parts: critical assessment of current protein domain identification programs, and development of approaches to improve the accuracy by combining existing computer programs. Students interested in AJAX web development are also invited to help improve the web interface for displaying current data on protein domain architectures.

Day-to-day supervisor for this project: John-Marc Chandonia, Staff Researcher

Qualifications: The ideal candidate is willing to learn and knows how to write programs (in any language); knowledge of protein structures is a plus. Applicants with GPA under 3.6 will be considered only in exceptional circumstances. Other requirements: Candidates must: •Attend 3-hour lab meeting every week •Attend personal genomics subgroup meeting every week •Adhere to all lab policies (including weekly notebooks to track research, semester reports) •Must register for credits, regardless of program-specific requirements. Our lab is interdisciplinary, and we want to get a holistic picture of your background, so please include your unofficial transcript with your application.

Weekly Hours: 9-12 hrs

Related website: http://compbio.berkeley.edu

Closed (4) Critical Assessment of Genome Interpretation (CAGI)

Closed. This professor is continuing with Spring 2017 apprentices on this project; no new apprentices needed for Fall 2017.

The enormous flood of genomic data resulting from the greatly increased sequencing capacity and decreasing cost presents an enormous challenge to all of us who are trying to understand, interpret and extract useful information from genomic data. The field of genome interpretation is essential for the advancement of personalized medicine. Multiple studies have highlighted the pathogenicity of rare coding variants in diseases and such variants might contribute to the missing heritability from genome wide association studies. However, the majority of variation discovered by the next generations sequencing technology is of unknown significance. Variants of uncertain significance represent one of the greatest current challenge in clinical genetics, and the availability of individuals’ whole genomes has vastly increased the ascertainment of such variants without comparably aiding their interpretation. Many successful genome interpretation studies have been published, and in the clinic, exome and genome sequencing are increasingly being used to improve prevention, diagnosis, treatment and understanding of human diseases. Yet, the field lacks a clear consensus on what kind of methods provide useful tools to interpret the data.

The Critical Assessment of Genome Interpretation (CAGI) is a community experiment to evaluate the prediction of phenotypes from genetic variation. CAGI objectively assesses computational methods for predicting the phenotypic impacts of genomic variation. In this experiment, participants are provided genotypic data and make predictions of resulting molecular, cellular, or organismal phenotype. These predictions are evaluated against experimental and clinical characterizations, and independent assessors perform the evaluations. Community workshops are held to disseminate results, assess our collective ability to make accurate and meaningful phenotypic predictions, and better understand progress in the field. From this experiment, we identify bottlenecks in genome interpretation, inform critical areas of future research, and connect researchers from diverse disciplines whose expertise is essential to methods for genome interpretation.


You will work closely with Gaia Andreoletti (the CAGI Coordinating Postdoctoral Researcher) in operating the CAGI experiment and in evaluating the capability of state-of-art methods to make useful predictions from genetic data. The project will entail in a detailed literature review of the current available methods for variant annotation and the assessment of the specificity and sensitivity of these methods to assess and categorize disease causal mutations from neutral mutations using real patient whole genome sequence data.

Day-to-day supervisor for this project: Gaia Andreoletti, Post-Doc

Qualifications: The ideal candidate is willing to learn and knows how to write programs (in any language); knowledge of genomic is a plus. Applicants with GPA under 3.6 will be considered only in exceptional circumstances. Other requirements: Candidates must: •Attend 3-hour lab meeting every week •Attend personal genomics subgroup meeting every week •Adhere to other lab policy (including weekly notebooks to track research, semester reports) •Register for credits, regardless of their program-specific requirements.

Weekly Hours: 9-12 hrs

Related website: https://genomeinterpretation.org
Related website: http://compbio.berkeley.edu/