Large-scale computational prediction of protein structures represents a cost-effective alternative to empirical structure determination with particular promise for non-model organisms and neglected pathogens. Conventional sequence-based tools are insufficient to annotate the genomes of such divergent biological systems. Conversely, protein structure tolerates substantial variation in primary amino acid sequence, and is thus a robust indicator of biochemical function. Structural proteomics is poised to become a standard part of pathogen genomics research, however informatic methods are now required to assign confidence in large volumes of predicted structures.
To predict the proteome of a neglected human pathogen, Giardia duodenalis, and stratify predicted structures into high- and lower-confidence categories using a variety of metrics in isolation and combination.
We used the I-TASSER suite to predict structural models for ∼5000 proteins encoded in Giardia duodenalis and identify their closest empirically determined structural homologues in the Protein Data Bank. Models were assigned to high or lower-confidence categories depending on the presence of matching PFAM domains in query and reference peptides. Metrics output from the suite and derived metrics were assessed for their ability to predict the high confidence category individually, and in combination through development of a random forest classifier.
We identified 1095 high confidence models including 212 hypothetical proteins. Amino acid identity between query and reference peptides was the greatest individual predictor of high confidence status, however the random forest classifier out-performed any metric in isolation (AUC = 0.977), and identified a subset of 305 high confidence-like models, corresponding to false positive predictions. High confidence models exhibited higher transcriptional abundance, and the classifier generalized across species, indicating the broad utility of this approach for automatically stratifying predicted structures. Additional structure-based clustering was used to cross-check confidence predictions in an expanded family of Nek kinases. Several high confidence-like proteins yielded substantial new insight into mechanisms of redox balance in Giardia duodenalis—a system central to the efficacy of limited anti-giardial drugs.
Structural proteomics combined with machine learning can aid genome annotation for genetically divergent organisms including human pathogens, and stratify predicted structures to promote efficient allocation of limited resources for experimental investigation.
Breast cancer risk for BRCA1 and BRCA2 pathogenic mutation carriers is modified by risk factors that cluster in families, including genetic modifiers of risk. We considered genetic modifiers of risk for carriers of high-risk mutations in other breast cancer susceptibility genes.
In a family known to carry the high-risk mutation PALB2:c.3113G>A (p.Trp1038*), whole-exome sequencing was performed on germline DNA from four affected women, three of whom were mutation carriers.
RNASEL:p.Glu265* was identified in one of the PALB2 carriers who had two primary invasive breast cancer diagnoses before 50 years. Gene-panel testing of BRCA1, BRCA2, PALB2 and RNASEL in the Australian Breast Cancer Family Registry identified five carriers of RNASEL:p.Glu265* in 591 early onset breast cancer cases. Three of the five women (60%) carrying RNASEL:p.Glu265* also carried a pathogenic mutation in a breast cancer susceptibility gene compared with 30 carriers of pathogenic mutations in the 586 non-carriers of RNASEL:p.Glu265* (5%) (p < 0.002). Taqman genotyping demonstrated that the allele frequency of RNASEL:p.Glu265* was similar in affected and unaffected Australian women, consistent with other populations.
Our study suggests that RNASEL:p.Glu265* may be a genetic modifier of risk for early-onset breast cancer predisposition in carriers of high-risk mutations. Much larger case-case and case-control studies are warranted to test the association observed in this report.
Neural injury triggers swift responses from glia, including glial migration and phagocytic clearance of damaged neurons. The transcriptional programs governing these complex innate glial immune responses are still unclear. Here, we describe a novel injury assay in adult Drosophila that elicits widespread glial responses in the ventral nerve cord (VNC). We profiled injury-induced changes in VNC gene expression by RNA sequencing (RNA-seq) and found that responsive genes fall into diverse signaling classes. One factor, matrix metalloproteinase-1 (MMP-1), is induced in Drosophila ensheathing glia responding to severed axons. Interestingly, glial induction of MMP-1 requires the highly conserved engulfment receptor Draper, as well as AP-1 and STAT92E. In MMP-1 depleted flies, glia do not properly infiltrate neuropil regions after axotomy and, as a consequence, fail to clear degenerating axonal debris. This work identifies Draper-dependent activation of MMP-1 as a novel cascade required for proper glial clearance of severed axons.
Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a ‘life cycle’ view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain.
Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on ‘omics’ datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.
Background:Previously, we described ROVER, a DNA variant caller which identifies genetic variants from PCR-targeted massively parallel sequencing (MPS) datasets generated by the Hi-Plex protocol. ROVER permits stringent filtering of sequencing chemistry-induced errors by requiring reported variants to appear in both reads of overlapping pairs above certain thresholds of occurrence. ROVER was developed in tandem with Hi-Plex and has been used successfully to screen for genetic mutations in the breast cancer predisposition gene PALB2.
ROVER is applied to MPS data in BAM format and, therefore, relies on sequence reads being mapped to a reference genome. In this paper, we describe an improvement to ROVER, called UNDR ROVER (Unmapped primer-Directed ROVER), which accepts MPS data in FASTQ format, avoiding the need for a computationally expensive mapping stage. It does so by taking advantage of the location-specific nature of PCR-targeted MPS data.
Results: The UNDR ROVER algorithm achieves the same stringent variant calling as its predecessor with a significant runtime performance improvement. In one indicative sequencing experiment, UNDR ROVER (in its fastest mode) required 8-fold less sequential computation time than the ROVER pipeline and 13-fold less sequential computation time than a variant calling pipeline based on the popular GATK tool.
UNDR ROVER is implemented in Python and runs on all popular POSIX-like operating systems (Linux, OS X). It requires as input a tab-delimited format file containing primer sequence information, a FASTA format file containing the reference genome sequence, and paired FASTQ files containing sequence reads. Primer sequences at the 5′ end of reads associate read-pairs with their targeted amplicon and, thus, their expected corresponding coordinates in the reference genome. The primer-intervening sequence of each read is compared against the reference sequence from the same location and variants are identified using the same algorithm as ROVER. Specifically, for a variant to be ‘called’ it must appear at the same location in both of the overlapping reads above user-defined thresholds of minimum number of reads and proportion of reads.
Conclusions: UNDR ROVER provides the same rapid and accurate genetic variant calling as its predecessor with greatly reduced computational costs.
Background: The NCBI Entrez Gene and PubMed databases contain a wealth of high-quality information about genes for many different organisms. The NCBI Entrez online web-search interface is convenient for simple manual search for a small number of genes but impractical for the kinds of outputs seen in typical genomics projects.
Results: We have developed an efficient open source tool implemented in Python called Annokey, which annotates gene lists with the results of a keyword search of the NCBI Entrez Gene database and linked Pubmed article information. The user steers the search by specifying a ranked list of keywords (including multi-word phrases and regular expressions) that are correlated with their topic of interest. Rank information of matched terms allows the user to guide further investigation.
We applied Annokey to the entire human Entrez Gene database using the key-term “DNA repair” and assessed its performance in identifying the 176 members of a published “gold standard” list of genes established to be involved in this pathway. For this test case we observed a sensitivity and specificity of 97% and 96%, respectively.
Conclusions: Annokey facilitates the identification of genes related to an area of interest, a task which can be onerous if performed manually on a large number of genes. Annokey provides a way to capitalize on the high quality information provided by the Entrez Gene database allowing both scalability and compatibility with automated analysis pipelines, thus offering the potential to significantly enhance research productivity.
This thesis is about the design and implementation of a debugging tool which helps Haskell programmers understand why their programs do not work as intended. The traditional debugging technique of examining the program execution step-by-step, popular with imperative languages, is less suitable for Haskell because its unorthodox evaluation strategy is difficult to relate to the structure of the original program source code. We build a debugger which focuses on the high-level logical meaning of a program rather than its evaluation order. This style of debugging is called declarative debugging, and it originated in logic programming languages. At the heart of the debugger is a tree which records information about the evaluation of the program in a manner which is easy to relate to the structure of the program. Links between nodes in the tree reflect logical relationships between entities in the source code. An error diagnosis algorithm is applied to the tree in a top-down fashion, searching for causes of bugs. The search is guided by an oracle, who knows how each part of the program should behave. The oracle is normally a human — typically the person who wrote the program — however, much of its behaviour can be encoded in software.
An interesting aspect of this work is that the debugger is implemented by means of a program transformation. That is, the program which is to be debugged is trans- formed into a new one, which when evaluated, behaves like the original program but also produces the evaluation tree as a side-effect. The transformed program is augmented with code to perform the error diagnosis on the tree. Running the trans- formed program constitutes the evaluation of the original program plus a debugging session. The use of program transformation allows the debugger to take advantage of existing compiler technology — a whole new compiler and runtime environment does not need to be written — which saves much work and enhances portability.
The technology described in this thesis is well-tested by an implementation in software. The result is a useful tool, called buddha, which is publicly available and supports all of the Haskell 98 standard.
Haskell is a very safe language, particularly because of its type system. However there will always be programs that do the wrong thing. Programmer fallibility, partial or incorrect specifications and typographic errors are but a few of the reasons that make bugs a fact of life. This paper is about the use and implementation of a debugger, called Buddha, which helps Haskell programmers understand why their programs misbehave. Traditional debugging tools that examine the program execution step-by-step are not suitable for Haskell because of its unorthodox evaluation strategy. Instead, a different approach is taken which abstracts away the evaluation order of the program and focuses on its high-level logical meaning.
This style of debugging is called Declarative Debugging, and it has its roots in the Logic Programming community. At the heart of the debugger is a tree which records information about the evaluation of the program in a manner which is easy to relate to the structure of the source code. It resembles a call graph annotated with the arguments and results of function applications, shown in their most evaluated form. Logical relationships between entities in the source are reflected in the links between nodes in the tree. An error diagnosis algorithm is applied to the tree in a top-down fashion in the search for causes of bugs.