Anomalies in natural populations of amphibians: Methodology for field studies

To be really efficient and conclusive, studies on the anomalies in natural populations of amphibians must be carried out in a perspective clearly centered on this topic rather than being a side product of works dealing with other questions. Recommendations are offered here on the methodology for such studies

Anomalies in Natural Populations of Amphibians: Methodology for Laboratory Studies

Recommendations are offered for the laboratory study of anomalies found in natural populations of amphibians to elucidate their causes. Various methods are mentioned, particularly breeding experiments, experimental gynogenesis and regeneration experiments

SSMART: Sequence-structure motif identification for RNA-binding proteins

MOTIVATION: RNA-binding proteins (RBPs) regulate every aspect of RNA metabolism and function. There are hundreds of RBPs encoded in the eukaryotic genomes, and each recognize its RNA targets through a specific mixture of RNA sequence and structure properties. For most RBPs, however, only a primary sequence motif has been determined, while the structure of the binding sites is uncharacterized. RESULTS: We developed SSMART, an RNA motif finder that simultaneously models the primary sequence and the structural properties of the RNA targets sites. The sequence-structure motifs are represented as consensus strings over a degenerate alphabet, extending the IUPAC codes for nucleotides to account for secondary structure preferences. Evaluation on synthetic data showed that SSMART is able to recover both sequence and structure motifs implanted into 3'UTR-like sequences, for various degrees of structured/unstructured binding sites. In addition, we successfully used SSMART on high-throughput in vivo and in vitro data, showing that we not only recover the known sequence motif, but also gain insight into the structural preferences of the RBP. AVAILABILITY: SSMART is freely available at https://ohlerlab.mdc-berlin.de/software/SSMART 137

SaTAnn quantifies translation on the functionally heterogeneous transcriptome

Deep sequencing methods have matured to comprehensively detect the full set of transcribed loci, but there is a gap to determine the function of the resulting highly complex transcriptomes. At the center of the gene expression cascade, translation is fundamental in defining the fate of much of the transcribed genome. We have developed a new approach (SaTAnn, Splice-aware Translatome Annotation) to annotate and quantify translation at the single open reading frame (ORF) level, that uses information from ribosome profiling to determine the translational state of each isoform in a comprehensive annotation. For most genes, one ORF represents the dominant translation product, but our approach also detects translation from ORFs belonging to multiple transcripts per gene, including targets of RNA surveillance mechanisms such as nonsense-mediated decay. Diversity in the translation output across human cell lines reveals the extent of gene-specific differences in protein production, which are supported by steady-state protein abundance estimates. Computational analysis of Ribo-seq data with SaTAnn (available at https://github.com/lcalviell/SaTAnn) provides a window into the functions of the heterogeneous transcriptom

COUGER-co-factors associated with uniquely-bound genomic regions

Most transcription factors (TFs) belong to protein families that share a common DNA binding domain and have very similar DNA binding preferences. However, many paralogous TFs (i.e. members of the same TF family) perform different regulatory functions and interact with different genomic regions in the cell. A potential mechanism for achieving this differential in vivo specificity is through interactions with protein co-factors. Computational tools for studying the genomic binding profiles of paralogous TFs and identifying their putative co-factors are currently lacking. Here, we present an interactive web implementation of COUGER, a classification-based framework for identifying protein co-factors that might provide specificity to paralogous TFs. COUGER takes as input two sets of genomic regions bound by paralogous TFs, and it identifies a small set of putative co-factors that best distinguish the two sets of sequences. To achieve this task, COUGER uses a classification approach, with features that reflect the DNA-binding specificities of the putative co-factors. The identified co-factors are presented in a user-friendly output page, together with information that allows the user to understand and to explore the contributions of individual co-factor features. COUGER can be run as a stand-alone tool or through a web interface: http://couger.oit.duke.edu

Designation of a Neotype for \u3cem\u3eLeptodactylus gracilis\u3c/em\u3e (Duméril and Bibron, 1840) (Amphibia: Leptodactylidae)

The examination of the preserved specimens in the herpetological collection of the Museum National d\u27Histoire Naturelle, along with a review of the original literature, indicates that no extant specimen can be undoubtedly identified as the holotype of Leptodactylus gracilis (DumCri.l and Bibron, 1840). Furthermore, it revealed that the type locality recently assigned to this taxon is in error

Designation of a Neotype for \u3cem\u3eLeptodactylus gracilis\u3c/em\u3e (Duméril and Bibron, 1840) (Amphibia: Leptodactylidae)

The examination of the preserved specimens in the herpetological collection of the Museum National d\u27Histoire Naturelle, along with a review of the original literature, indicates that no extant specimen can be undoubtedly identified as the holotype of Leptodactylus gracilis (DumCri.l and Bibron, 1840). Furthermore, it revealed that the type locality recently assigned to this taxon is in error

What You Need to Know About Moving Collections and Acquisitions Into an E‐Dominant Model!

Two different University of Maryland Libraries discuss how they have moved to an e‐dominant model, the reasons why, and the new acquisitions strategies libraries can use in crafting an e‐dominant collection. Whether your organization is a large ARL library like University of Maryland, College Park (UMD) Libraries or a nontraditional online library like the University of Maryland University College (UMUC) Library, there are many strategies for taking advantage of the new acquisitions environment and rethinking how to build collections in an e‐dominant world. At UMD, adopting an e‐dominant model has been a gradual change over time, allowing the library staff to develop new ideas about collection development and experiment with new tools and techniques for acquiring and managing the libraries’ collection. As these changes have unfolded over time, staff began to develop a more comprehensive and holistic picture, becoming more aware of how their own work with e‐resources impacts our colleagues, our patrons, and the wider library community. At the UMUC Library, the electronic resources management staff developed an e‐model initiative that represents a fundamental shift for electronic resources management at UMUC. Electronic resources have become a critical, important, and fully integrated component in course development for the university and this is driving the direction of collection development for the Library. The main thrust of this shift has been the establishment of an E‐Resources Initiative to replace the use of textbooks in print with e‐resources, primarily open access, embedded within the learning management system (LMS) course modules

Janggu: deep learning for genomics

MOTIVATION: In recent years, numerous applications have demonstrated the potential of deep learning for an improved understanding of biological processes. However, most deep learning tools developed so far are designed to address a specific question on a fixed dataset and/or by a fixed model architecture. Adapting these models to integrate new datasets or to address different hypotheses can lead to considerable software engineering effort. To address this aspect we have built Janggu, a python library that facilitates deep learning for genomics applications. RESULTS: Janggu aims to ease data acquisition and model evaluation in multiple ways. Among its key features are special dataset objects, which form a unified and flexible data acquisition and pre-processing framework for genomics data that enables streamlining of future research applications through reusable components. Through a numpy-like interface, the dataset objects are directly compatible with popular deep learning libraries, including keras. Furthermore, Janggu offers the possibility to visualize predictions as genomic tracks or by exporting them to the BIGWIG format. We illustrate the functionality of Janggu on several deep learning genomics applications. First, we evaluate different model topologies for the task of predicting binding sites for the transcription factor JunD. Second, we demonstrate the framework on published models for predicting chromatin effects. Third, we show that promoter usage measured by CAGE can be predicted using DNase hyper-sensitivity, histone modifications and DNA sequence features. We improve the performance of these models due to a novel feature in Janggu that allows us to include high-order sequence features. We believe that Janggu will help to significantly reduce repetitive programming overhead for deep learning applications in genomics, while at the same time enabling computational biologists to assess biological hypotheses more rapidly. AVAILABILITY: Janggu is freely available under a GPL-v3 license on https://github.com/BIMSBbioinfo/janggu or via https://pypi.org/project/jangg