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Summary With the growing availability of population-scale whole-exome and whole-genome sequencing, demand for reproducible, scalable variant analysis has spread within genomic research communities. To address this need, we introduce the Python package Variant Analysis and Prioritization (VAPr). VAPr leverages existing annotation tools ANNOVAR and MyVariant.info with MongoDB-based flexible storage and filtering functionality. It offers biologists and bioinformatics generalists easy-to-use and scalable analysis and prioritization of genomic variants from large cohort studies. Availability and implementation VAPr is developed in Python and is available for free use and extension under the MIT License. An install package is available on PyPi at https://pypi.python.org/pypi/VAPr, while source code and extensive documentation are on GitHub at https://github.com/ucsd-ccbb/VAPr.

Despite the great diversity of vertebrate limb proportion and our deep understanding of the genetic mechanisms that drive skeletal elongation, little is known about how individual bones reach different lengths in any species. Here, we directly compare the transcriptomes of homologous growth cartilages of the mouse (Mus musculus) and bipedal jerboa (Jaculus jaculus), which has extremely long metatarsals of the feet and ‘mouse-like’ arms. When we intersected gene expression differences in metatarsals of the two species with expression differences in forearms, we found that about 10% of all orthologous genes are associated with disproportionate elongation of jerboa feet. Among these,Shox2, has gained expression in jerboa metatarsals where it is not expressed in other vertebrates that have been assessed. This transcription factor is necessary for proximal limb elongation, and we show that it is sufficient to increase mouse distal limb length. Unexpectedly, we also found evidence that jerboa foot elongation occurs in part by releasing latent growth potential that is repressed in mouse feet. In jerboa metatarsals, we observed higher expression ofCrabp1, an antagonist of growth inhibitory retinoic acid, lower expression ofGdf10, an inhibitory TGFβ ligand, and lower expression ofMab21L2, a BMP signaling inhibitor that we show is sufficient to reduce limb bone elongation. By intersecting our data with prior expression analyses in other systems, we identify mechanisms that may both establish limb proportion during development and diversify proportion during evolution. The genes we identified here therefore provide a framework to understand the modular genetic control of skeletal growth and the remarkable malleability of vertebrate limb proportion.

Motivation Network biology is widely used to elucidate mechanisms of disease and biological processes. The ability to interact with biological networks is important for hypothesis generation and to give researchers an intuitive understanding of the data. We present visJS2jupyter, a tool designed to embed interactive networks in Jupyter notebooks to streamline network analysis and to promote reproducible research. Results The tool provides functions for performing and visualizing useful network operations in biology, including network overlap, network propagation around a focal set of genes, and co-localization of two sets of seed genes. visJS2jupyter uses the JavaScript library vis.js to create interactive networks displayed within Jupyter notebook cells with features including drag, click, hover, and zoom. We demonstrate the functionality of visJS2jupyter applied to a biological question, by creating a network propagation visualization to prioritize risk-related genes in autism. Availability and implementation The visJS2jupyter package is distributed under the MIT License. The source code, documentation and installation instructions are freely available on GitHub at https://github.com/ucsd-ccbb/visJS2jupyter. The package can be downloaded at https://pypi.python.org/pypi/visJS2jupyter. Supplementary information Supplementary data are available at Bioinformatics online.

Genetic interactions, in particular negative or "synthetic-lethal" interactions for which simultaneous disruption of two genes causes cell killing, have implications for therapeutic development as has been demonstrated by the clinical success of PARP inhibitors specifically for tumors with loss-of-function mutations in BRCA1/2. However, further applications of synthetic-lethal cancer therapy have been limited by poor understanding of the important genetic interactions in a cancer cell, and how these vary from one cancer type to another or from patient to patient. To enable systematic mapping of these genetic interaction networks, we recently developed a CRISPR-Cas9 screening methodology for knocking out single and pairs of genes in high throughput. Critical to this method is the precise determination of single-gene knockout effects, which is accomplished by serial measurement of the relative changes in gRNAs at days 3, 14, 21 and 28 post-transduction. To robustly quantify gene fitness and genetic interactions we developed a novel computational analysis framework that integrates all samples across the multiple days of the experiment; with said method we achieve Pearson correlation of 0.95 or greater between biologic replicates in the same cell line (p < 1x10-30). Additionally we demonstrate that our analysis method is robust to the compositional effects inherent in a pooled knockout experiment. To facilitate reproducibility of analyses and distribution to the scientific community, the code has been packaged into a modular series of python notebooks freely available on github. Evaluating all pairwise gene knockout combinations among a panel of 73 genes divided between tumor-suppressor genes (TSG) and cancer-relevant drug targets (DT) in a total of 5 cancer cell lines from diverse lineages (HeLa, A549, 293T, U2OS, LN229), we identified 226 synthetic lethal and 14 epistatic interactions at a Z-score cut-off of -3 (FDR ~0.3). Of the synthetic lethal interactions 203 (89.8%) were private to a single cell line, and no interaction was seen in more than 3 of 5 five cell lines. Thus far 10 (out of 16 tested) therapeutically relevant interactions have been replicated in low-throughput assays using either combinatorial drugs or CRISPR knockout of a TSG paired with a drug (71% precision or positive predictive value). The cell line specificity of interactions was also confirmed in low-throughput assays (75% negative predictive value). In summary, we have discovered many therapeutically relevant genetic interactions in cancer and identified the great importance of cellular context on the architecture of the genetic interaction network. Recognizing that there will be great diversity in genetic interaction between different tumors, it will be important to perform future studies across a large number of samples, which is enabled by the high-throughput method we have developed. Citation Format: John Paul Shen, Dongxin Zhao, Brenton Munson, Amanda Birmingham, Roman Sasik, Ana Bojorquez-Gomez, Katherine Licon, Kristin Klepper, Alex Beckett, Kyle Salinas Sanchez, Prashant Mali, Trey Ideker. High-throughput combinatorial CRISPR-Cas9 gene knockout reveals most genetic interactions are context dependent [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3299.

The metabolic pathways fueling tumor growth have been well characterized, but the specific impact of transforming events on network topology and enzyme essentiality remains poorly understood. To this end, we performed combinatorial CRISPR-Cas9 screens on a set of 51 carbohydrate metabolism genes that represent glycolysis and the pentose phosphate pathway. This high-throughput methodology enabled systems-level interrogation of metabolic gene dispensability, interactions, and compensation across multiple cell types. The metabolic impact of specific combinatorial knockouts were validated using 13C and 2H isotope tracing, and, these assays together revealed key nodes controlling redox homeostasis along the KEAP1-NRF2 signaling axis. Specifically, targeting KEAP1 in combination with oxidative PPP enzymes mitigated the deleterious effects of these knockouts on growth rates. These results demonstrate how our integrated framework, combining genetic, transcriptomic, and flux measurements, can improve elucidation of metabolic network alterations, and guide precision targeting of metabolic vulnerabilities based on tumor genetics.

Effective gene silencing by the RNA interference (RNAi) pathway requires a comprehensive understanding of the elements that influence small interfering RNA (siRNA) functionality and specificity. These include (i) sequence space restrictions that define the boundaries of siRNA targeting, (ii) structural and sequence features required for efficient siRNA performance, (iii) mechanisms that underlie nonspecific gene modulation and (iv) additional features specific to the intended use (i.e., inclusion of native sugar or base chemical modifications for increased stability or specificity, vector design, etc.). Attention to each of these factors enhances siRNA performance and heightens overall confidence in the output of RNAi-mediated functional genomic studies. Here, we provide a detailed protocol explaining the methodologies used for manual and web-based design of siRNAs.

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

International audience; Multiple sequence alignments are powerful tools for understanding the structures, functions, and evolutionary histories of linear biological macromolecules (DNA, RNA, and proteins), and for finding homologs in sequence databases. We address several ontological issues related to RNA sequence alignments that are informed by structure. Multiple sequence alignments are usually shown as two-dimensional (2D) matrices, with rows representing individual sequences, and columns identifying nucleotides from different sequences that correspond structurally, functionally, and/or evolutionarily. However, the requirement that sequences and structures correspond nucleotide-by-nucleotide is unrealistic and hinders representation of important biological relationships. High-throughput sequencing efforts are also rapidly making 2D alignments unmanageable because of vertical and horizontal expansion as more sequences are added. Solving the shortcomings of traditional RNA sequence alignments requires explicit annotation of the meaning of each relationship within the alignment. We introduce the notion of "correspondence," which is an equivalence relation between RNA elements in sets of sequences as the basis of an RNA alignment ontology. The purpose of this ontology is twofold: first, to enable the development of new representations of RNA data and of software tools that resolve the expansion problems with current RNA sequence alignments, and second, to facilitate the integration of sequence data with secondary and three-dimensional structural information, as well as other experimental information, to create simultaneously more accurate and more exploitable RNA alignments.

RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.

Pairing between the hexamer seed region of a small interfering RNA (siRNA) guide strand (nucleotides 2–7) and complementary sequences in the 3′ UTR of mature transcripts has been implicated as an important element in off-target gene regulation and false positive phenotypes. To better understand the association between seed sequences and off-target profiles we performed an analysis of all possible (4096) hexamers and identified a nonuniform distribution of hexamer frequencies across the 3′ UTR transcriptome. Subsequent microarray analysis of cells transfected with siRNAs having seeds with low, medium, or high seed complement frequencies (SCFs) revealed that duplexes with low SCFs generally induced fewer off-targets and off-target phenotypes than molecules with more abundant 3′ UTR complements. These findings provide the first experimentally validated strategy for designing siRNAs with enhanced specificity and allow for more accurate interpretation of high throughput screening data generated with existing siRNA/shRNA collections.

Background: Advanced intercross lines (AIL) are segregating populations created using a multi-generation breeding protocol for fine mapping complex trait loci (QTL) in mice and other organisms. Applying QTL mapping methods for intercross and backcross populations, often followed by na ve permutation of individuals and phenotypes, does not account for the effect of AIL family structure in which final generations have been expanded and leads to inappropriately low significance thresholds. The critical problem with na ve mapping approaches in AIL populations is that the individual is not an exchangeable unit. Methodology/Principal Findings: The effect of family structure has immediate implications for the optimal AIL creation (many crosses, few animals per cross, and population expansion before the final generation) and we discuss these and the utility of AIL populations for QTL fine mapping. We also describe Genome Reshuffling for Advanced Intercross Permutation, (GRAIP) a method for analyzing AIL data that accounts for family structure. GRAIP permutes a more interchangeable unit in the final generation crosses - the parental genome - and simulating regeneration of a permuted AIL population based on exchanged parental identities. GRAIP determines appropriate genome-wide significance thresholds and locus-specific Pvalues for AILs and other populations with similar family structures. Wemore » contrast GRAIP with na ve permutation using a large densely genotyped mouse AIL population (1333 individuals from 32 crosses). A na ve permutation using coat color as a model phenotype demonstrates high false-positive locus identification and uncertain significance levels, which are corrected using GRAIP. GRAIP also detects an established hippocampus weight locus and a new locus, Hipp9a. Conclusions and Significance: GRAIP determines appropriate genome-wide significance thresholds and locus-specific Pvalues for AILs and other populations with similar family structures. The effect of family structure has immediate implications for the optimal AIL creation and we discuss these and the utility of AIL populations.« less

Off-target gene silencing can present a notable challenge in the interpretation of data from large-scale RNA interference (RNAi) screens. We performed a detailed analysis of off-targeted genes identified by expression profiling of human cells transfected with small interfering RNA (siRNA). Contrary to common assumption, analysis of the subsequent off-target gene database showed that overall identity makes little or no contribution to determining whether the expression of a particular gene will be affected by a given siRNA, except for near-perfect matches. Instead, off-targeting is associated with the presence of one or more perfect 3' untranslated region (UTR) matches with the hexamer or heptamer seed region (positions 2-7 or 2-8) of the antisense strand of the siRNA. These findings have strong implications for future siRNA design and the application of RNAi in high-throughput screening and therapeutic development.

Nat. Methods 3, 199–204 (2006). In Figure 3 of the original article, the genes in panels a–c were incorrectly identified. The correct gene names are: (a) Ppyr/LUC siRNA A, (b) ALPPL2 and (c) Ppyr/LUC siRNA B. The figure legend should read: Figure 3 | Systematic single base pair–mismatch analysis of siRNA functionality.