19 research outputs found
Biases in the Experimental Annotations of Protein Function and their Effect on Our Understanding of Protein Function Space
The ongoing functional annotation of proteins relies upon the work of
curators to capture experimental findings from scientific literature and apply
them to protein sequence and structure data. However, with the increasing use
of high-throughput experimental assays, a small number of experimental studies
dominate the functional protein annotations collected in databases. Here we
investigate just how prevalent is the "few articles -- many proteins"
phenomenon. We examine the experimentally validated annotation of proteins
provided by several groups in the GO Consortium, and show that the distribution
of proteins per published study is exponential, with 0.14% of articles
providing the source of annotations for 25% of the proteins in the UniProt-GOA
compilation. Since each of the dominant articles describes the use of an assay
that can find only one function or a small group of functions, this leads to
substantial biases in what we know about the function of many proteins.
Mass-spectrometry, microscopy and RNAi experiments dominate high throughput
experiments. Consequently, the functional information derived from these
experiments is mostly of the subcellular location of proteins, and of the
participation of proteins in embryonic developmental pathways. For some
organisms, the information provided by different studies overlap by a large
amount. We also show that the information provided by high throughput
experiments is less specific than those provided by low throughput experiments.
Given the experimental techniques available, certain biases in protein function
annotation due to high-throughput experiments are unavoidable. Knowing that
these biases exist and understanding their characteristics and extent is
important for database curators, developers of function annotation programs,
and anyone who uses protein function annotation data to plan experiments.Comment: Accepted to PLoS Computational Biology. Press embargo applies. v4:
text corrected for style and supplementary material inserte
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Internship Experiences Contribute to Confident Career Decision Making for Doctoral Students in the Life Sciences.
The Graduate Student Internships for Career Exploration (GSICE) program at the University of California, San Francisco (UCSF), offers structured training and hands-on experience through internships for a broad range of PhD-level careers. The GSICE program model was successfully replicated at the University of California, Davis (UC Davis). Here, we present outcome data for a total of 217 PhD students participating in the UCSF and UC Davis programs from 2010 to 2015 and 2014 to 2015, respectively. The internship programs at the two sites demonstrated comparable participation, internship completion rates, and overall outcomes. Using survey, focus group, and individual interview data, we find that the programs provide students with career development skills, while increasing students' confidence in career exploration and decision making. Internships, in particular, were perceived by students to increase their ability to discern a career area of choice and to increase confidence in pursuing that career. We present data showing that program participation does not change median time to degree and may help some trainees avoid "default postdocs." Our findings suggest important strategies for institutions developing internship programs for PhD students, namely: including a structured training component, allowing postgraduation internships, and providing a central organization point for internship programs
The Structure-Function Linkage Database
The Structure–Function Linkage Database (SFLD, http://sfld.rbvi.ucsf.edu/) is a manually curated classification resource describing structure–function relationships for functionally diverse enzyme superfamilies. Members of such superfamilies are diverse in their overall reactions yet share a common ancestor and some conserved active site features associated with conserved functional attributes such as a partial reaction. Thus, despite their different functions, members of these superfamilies ‘look alike’, making them easy to misannotate. To address this complexity and enable rational transfer of functional features to unknowns only for those members for which we have sufficient functional information, we subdivide superfamily members into subgroups using sequence information, and lastly into families, sets of enzymes known to catalyze the same reaction using the same mechanistic strategy. Browsing and searching options in the SFLD provide access to all of these levels. The SFLD offers manually curated as well as automatically classified superfamily sets, both accompanied by search and download options for all hierarchical levels. Additional information includes multiple sequence alignments, tab-separated files of functional and other attributes, and sequence similarity networks. The latter provide a new and intuitively powerful way to visualize functional trends mapped to the context of sequence similarity
Annotation Error in Public Databases: Misannotation of Molecular Function in Enzyme Superfamilies
Due to the rapid release of new data from genome sequencing projects, the majority of protein sequences in public databases have not been experimentally characterized; rather, sequences are annotated using computational analysis. The level of misannotation and the types of misannotation in large public databases are currently unknown and have not been analyzed in depth. We have investigated the misannotation levels for molecular function in four public protein sequence databases (UniProtKB/Swiss-Prot, GenBank NR, UniProtKB/TrEMBL, and KEGG) for a model set of 37 enzyme families for which extensive experimental information is available. The manually curated database Swiss-Prot shows the lowest annotation error levels (close to 0% for most families); the two other protein sequence databases (GenBank NR and TrEMBL) and the protein sequences in the KEGG pathways database exhibit similar and surprisingly high levels of misannotation that average 5%–63% across the six superfamilies studied. For 10 of the 37 families examined, the level of misannotation in one or more of these databases is >80%. Examination of the NR database over time shows that misannotation has increased from 1993 to 2005. The types of misannotation that were found fall into several categories, most associated with “overprediction” of molecular function. These results suggest that misannotation in enzyme superfamilies containing multiple families that catalyze different reactions is a larger problem than has been recognized. Strategies are suggested for addressing some of the systematic problems contributing to these high levels of misannotation
Internship Experiences Contribute to Confident Career Decision Making for Doctoral Students in the Life Sciences.
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Recommended from our members
Internship Experiences Contribute to Confident Career Decision Making for Doctoral Students in the Life Sciences.
The Graduate Student Internships for Career Exploration (GSICE) program at the University of California, San Francisco (UCSF), offers structured training and hands-on experience through internships for a broad range of PhD-level careers. The GSICE program model was successfully replicated at the University of California, Davis (UC Davis). Here, we present outcome data for a total of 217 PhD students participating in the UCSF and UC Davis programs from 2010 to 2015 and 2014 to 2015, respectively. The internship programs at the two sites demonstrated comparable participation, internship completion rates, and overall outcomes. Using survey, focus group, and individual interview data, we find that the programs provide students with career development skills, while increasing students' confidence in career exploration and decision making. Internships, in particular, were perceived by students to increase their ability to discern a career area of choice and to increase confidence in pursuing that career. We present data showing that program participation does not change median time to degree and may help some trainees avoid "default postdocs." Our findings suggest important strategies for institutions developing internship programs for PhD students, namely: including a structured training component, allowing postgraduation internships, and providing a central organization point for internship programs