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Background. Excision tissue biopsy, while central to cancer treatment and precision medicine, presents risks to the patient and does not provide a sufficiently broad and faithful representation of the heterogeneity of sampled solid tumors. Methods. Here we introduce e-biopsy – a novel concept for molecular profiling of solid tumors using molecular extraction with electroporation. As e-biopsy provides access to the molecular composition of a solid tumor, it potentially facilitates tumor diagnostics without tissue resection. Furthermore, thanks to its less invasive characteristics, e-biopsy enables probing the solid tumor multiple times in several distinct locations in the same procedure, thereby enabling the spatial profiling of tumor molecular heterogeneity.Results. We demonstrate e-biopsy in vivo, using the 4T1 breast cancer model in mice to assess its performance, as well as the inferred spatial differential protein expression. In particular, we show that proteomic profiles obtained via e-biopsy in vivo distinguish the tumors from healthy tissue and reflect spatial tumor differential protein expression.Conclusions. E-biopsy provides a completely new molecular cartography modality for solid tumors, providing information that potentially enables more sensitive detection, at lesser risk, as well as more precise personalized medicine.

Different miRNA profiling protocols and technologies introduce differences in the resulting quantitative expression profiles. These include differences in the presence (and measurability) of certain miRNAs. We present and examine a method based on quantile normalization, Adjusted Quantile Normalization (AQuN), to combine miRNA expression data from multiple studies in breast cancer into a single joint dataset for integrative analysis. By pooling multiple datasets, we obtain increased statistical power, surfacing patterns that do not emerge as statistically significant when separately analyzing these datasets. To merge several datasets, as we do here, one needs to overcome both technical and batch differences between these datasets. We compare several approaches for merging and jointly analyzing miRNA datasets. We investigate the statistical confidence for known results and highlight potential new findings that resulted from the joint analysis using AQuN. In particular, we detect several miRNAs to be differentially expressed in estrogen receptor (ER) positive versus ER negative samples. In addition, we identify new potential biomarkers and therapeutic targets for both clinical groups. As a specific example, using the AQuN-derived dataset we detect hsa-miR-193b-5p to have a statistically significant over-expression in the ER positive group, a phenomenon that was not previously reported. Furthermore, as demonstrated by functional assays in breast cancer cell lines, overexpression of hsa-miR-193b-5p in breast cancer cell lines resulted in decreased cell viability in addition to inducing apoptosis. Together, these observations suggest a novel functional role for this miRNA in breast cancer. Packages implementing AQuN are provided for Python and Matlab: https://github.com/YakhiniGroup/PyAQN., Author summary This work demonstrates a practical approach to the joint-analysis of multiple miRNA expression profiling datasets acquired with different measurement technologies. The use of different platforms in miRNA profiling can lead to major differences in results. In particular, some miRNA species are less amenable to detection and quantification by certain platforms or designs. Our approach, termed AQuN, combines quantile normalization with special attention to missing entities, to normalize miRNA expression across datasets, technologies, designs and platforms. As we show, our proposed approach uncovers patterns of interest that would not have emerged as statistically significant when analyzing the datasets individually or with other standard-practice normalization methods. Amongst our findings, we noted a previously undocumented miRNA that is significantly over-expressed in samples from estrogen-receptor positive breast cancer patients as compared to samples from estrogen-receptor negative patients. We further investigated this miRNA, hsa-miR-193b-5p, and experimentally show, in cell lines, that its expression level impacts the viability of tumor cells. AQuN is available to the community in the form of Python and Matlab packages. The joint-processed data is also made available for further investigation.

Genome conformation capture techniques permit a systematic investigation into the functional spatial organization of genomes, including functional aspects like assessing the co-localization of sets of genomic elements. For example, the co-localization of genes targeted by a transcription factor (TF) within a transcription factory. We quantify spatial co-localization using a rigorous statistical model that measures the enrichment of a subset of elements in neighbourhoods inferred from Hi-C data. We also control for co-localization that can be attributed to genomic order. We systematically apply our open-sourced framework, spatial-mHG, to search for spatial co-localization phenomena in multiple unicellular Hi-C datasets with corresponding genomic annotations. Our biological findings shed new light on the functional spatial organization of genomes, including: In C. crescentus, DNA replication genes reside in two genomic clusters that are spatially co-localized. Furthermore, these clusters contain similar gene copies and lay in genomic vicinity to the ori and ter sequences. In S. cerevisae, Ty5 retrotransposon family element spatially co-localize at a spatially adjacent subset of telomeres. In N. crassa, both Proteasome lid subcomplex genes and protein refolding genes jointly spatially co-localize at a shared location. An implementation of our algorithms is available online.

This paper describes an algorithm designed for Microsoft's Groove music service, which serves millions of users world wide. We consider the problem of automatically generating personalized music playlists based on queries containing a ``seed'' artist and the listener's user ID. Playlist generation may be informed by a number of information sources including: user specific listening patterns, domain knowledge encoded in a taxonomy, acoustic features of audio tracks, and overall popularity of tracks and artists. The importance assigned to each of these information sources may vary depending on the specific combination of user and seed~artist. The paper presents a method based on a variational Bayes solution for learning the parameters of a model containing a four-level hierarchy of global preferences, genres, sub-genres and artists. The proposed model further incorporates a personalization component for user-specific preferences. Empirical evaluations on both proprietary and public datasets demonstrate the effectiveness of the algorithm and showcase the contribution of each of its components.

While it has been long recognized that genes are not randomly positioned along the genome, the degree to which its 3D structure influences the arrangement of genes has remained elusive. In particular, several lines of evidence suggest that actively transcribed genes are spatially co-localized, forming transcription factories; however, a generalized systematic test has hitherto not been described. Here we reveal transcription factories using a rigorous definition of genomic structure based on Saccharomyces cerevisiae chromosome conformation capture data, coupled with an experimental design controlling for the primary gene order. We develop a data-driven method for the interpolation and the embedding of such datasets and introduce statistics that enable the comparison of the spatial and genomic densities of genes. Combining these, we report evidence that co-regulated genes are clustered in space, beyond their observed clustering in the context of gene order along the genome and show this phenomenon is significant for 64 out of 117 transcription factors. Furthermore, we show that those transcription factors with high spatially co-localized targets are expressed higher than those whose targets are not spatially clustered. Collectively, our results support the notion that, at a given time, the physical density of genes is intimately related to regulatory activity.

Motivation: Paired-end sequencing resulting in gapped short reads is commonly used for de novo genome assembly. Assembly methods use paired-end sequences in a two-step process, first treating each read-end independently, only later invoking the pairing to join the contiguous assemblies (contigs) into gapped scaffolds. Here, we present ELOPER, a pre-processing tool for pair-end sequences that produces a better read library for assembly programs. Results: ELOPER proceeds by simultaneously considering both ends of paired reads generating elongated reads. We show that ELOPER theoretically doubles read-lengths while halving the number of reads. We provide evidence that pre-processing read libraries using ELOPER leads to considerably improved assemblies as predicted from the Lander–Waterman model. Availability: http://sourceforge.net/projects/eloper. Contact: yanai@technion.ac.il Supplementary information: Supplementary data are available at Bioinformatics online.

Genotype–environment interactions were analyzed in C. elegans by identifying genes that respond differentially to environmental changes across five genotypes. These genes tend to be highly regulated and genomic analysis suggests that the interactions depend on changes in upstream regulators., Analysis of the transcriptomes of five C. elegans strains (genotype) cultivated in five growth conditions (environment) revealed genotype–environment interactions. Genes with genotype–environment interactions were distinguished by their longer intergenic lengths, high motif concentration, and mid-range expression levels. Genes with genotype–environment interactions do not significantly differ from background in terms of their promoter single-nucleotide polymorphisms. These results indicate that the highly regulated nature of particular genes predispose them for exhibiting genotype–environment interaction as a consequence of changes to upstream regulators., The phenotype of an organism is determined by its genotype and environment. An interaction between these two arises from the differential effect of the environment on gene expression in distinct genotypes; however, the genomic properties identifying these are not well understood. Here we analyze the transcriptomes of five C. elegans strains (genotype) cultivated in five growth conditions (environment), and find that highly regulated genes, as distinguished by intergenic lengths, motif concentration, and expression levels, are particularly biased toward genotype–environment interactions. Sequencing these strains, we find that genes with expression variation across genotypes are enriched for promoter single-nucleotide polymorphisms (SNPs), as expected. However, genes with genotype–environment interactions do not significantly differ from background in terms of their promoter SNPs. Collectively, these results indicate that the highly regulated nature of particular genes predispose them for exhibiting genotype–environment interaction as a consequence of changes to upstream regulators. This observation may provide a deeper understanding into the origin of the extraordinary gene expression diversity present in even closely related species.