dataesr/scientific-paragraphs-categorization huggingface dataset:
This large overlap with FaRLine was expected, because both are mapping-first approaches. This also shows that the differences between mapping-and assembly-first approaches reported above are not limited to one mapping-first approach. Besides exon-centric approaches, which aim at finding the differentially spliced exons, there is also a number of published methods which are isoform-centric and have the more ambitious goal to reconstruct full-length transcripts at the expense of underestimating alternative splicing. The most widely used mapping-first and isoform-centric approach is Cufflinks 6 that we compared to FaRLine using the same dataset. As shown in Fig. 5B  (and Supplementary Figure  S4B ), we found that the vast majority of ASE were predicted by both approaches. Finally, we compared KisSplice to one of the most widely used de-novo transcriptome assembler, Trinity  17.
Material and Methods FaRLine and KisSplice. Figure  1  shows the two pipelines that we are comparing. While STAR and TopHat are third-party softwares, we developed the other methods ourselves. KisSplice was first introduced in Sacomoto et al. 13. The novelty here is that its usage is now possible in the case where a reference genome is available, which required specific methodological developments implemented in the newly released KisSplice2RefGenome software. kissDE was first introduced in Lopez-Maestre et al. 24  in the context of SNPs for non-model species. We present here its extension for alternative splicing. FaRLine is a new mapping-first pipeline, that we introduce in this paper. It is the RNAseq pipeline associated to the FasterDB database  30  and was already successfully applied to the analysis of the effect of metformin treatment on myotonic dystrophy type I (DM1) with a validation rate of 95%  31.
Specifically, 20 cases of ASE regulated by the metformin treatment were tested, and 19 were validated. In this paper, we provide additional validations of FaRLine with similar validation rates (36 out of 38), Supplementary Figure  S19. For the sake of self-containment, we explain all methods here. KisSplice. KisSplice is a local transcriptome assembler. As most short reads transcriptome assemblers  8, 17, 32  , it relies on a De Bruijn graph (DBG). Its originality lies in the fact that it does not try to assemble full-length transcripts. Instead, it assembles the parts of the transcripts where there is a variation in the exon content. By aiming at a simpler goal, it can afford to be more exhaustive and identify more splicing events. The key concept on which KisSplice is built is that variations in the nucleotide content of the transcripts will correspond to specific patterns in the DBG called bubbles (Supplementary Figure  S13 ).
The cases of exact repeats correspond to recent gene duplications. FaRLine. FasterDB EnsEMBL r75 annotation: FasterDB RNAseq Pipeline, FaRLine, uses the FasterDB-based EnsEMBL r75 annotation database. FasterDB is a database containing all annotated human splicing variants  30. Each transcripts present in the FasterDB, is composed of a succession of exons, that we call transcript exons (represented in blue in Supplementary Figure  S15 ). The genomic exons (represented in red in Supplementary Figure  S15 ) are defined by projecting the transcript exons. First, the transcript exons are grouped by position. Then each group of exons defines a projected exon with the following rules: • The start is the leftmost start of the non-first-exon of the group. • The end is the rightmost end of the non-last-exon of the group that ends before the start of the next group of exons.
When the most frequent event annotated in the transcripts is an intron retention, the projected genomic exon is defined as a combination of the two exons flanking the retained intron. In Supplementary Figure  S15 , the exons 5 and 6 and the intron 5 are considered as one unique exon. As events included within one exon are not tested, this results in some events being missed. Mapping: The first step of FaRLine is to map the reads to a reference genome. This step is done using Tophat-2. 0. 11  6. tophat-min-intron-length 30-max-intron-length 1200000\-p 8 [-solexa1. 3-quals for Sknsh_rep1 and Sknsh_rep2]\-transcriptome-index A transcriptome index has been built by TopHat using EnsEMBL r75 annotations in gtf format. When a transcriptome index is used, the mapping steps are modified: instead of aligning first to the genome, which is the default behavior, TopHat uses Bowtie to align the reads to the transcript sequences first, then align the remaining unmapped reads to the genome.
To fasten the quantification step, a junction coordinate file with the corresponding read numbers is produced from the read alignment using the same filters than described above and will be used for all the quantification tools: junction, exon skipping, acceptor and donor. A challenge in defining the alternative skipped exon events is to identify the flanking exons. In the first version of FaRLine, these flankings exons were defined as the closest annotated genomic exons. This rule led to miss a lot of ASE events. Therefore, to define the flanking exons, we now use the information contained in the transcripts and in the reads. We consider each junction which skips an exon and is covered by at least one read. If this junction is annotated in the transcripts, we extract all annotated events containing this junction.
Quantification and differential analysis. Both pipelines perform ASE detection and quantification. The quantification step was done similarly in the two pipelines where only the junction reads were taken into account. To evaluate if using exonic reads in the quantification could increase the accuracy of our methods, we ran KisSplice on the MCF-7 dataset with the option -exonic reads set to on. In doing so, only the inclusion rate of the AS events changes. When comparing usage of only junction reads to usage of both junction and exonic reads, we observed that the p-values calculated strongly correlate as shown in Supplementary Figure  S16. We found that some AS events became significant upon the addition of exonic reads but the opposite also happened. Inspection of these events revealed that many are borderline cases, where the p-value is close, but slightly above 5%.
SK-N-SH dataset. We downloaded a total of 959 M reads from http://genome. crg. es/encode_RNA_dashboard/hg19/35. They correspond to long polyA+ RNAs generated by the Gingeras lab, and are also accessible with the following accession numbers (ENCSR000CPN -SRA: SRR315315, SRR315316 and ENCSR000CTT -SRA: SRR534309, SRR534310). For cell lines treated by retinoic acid, the reads were 76nt long, while they were 100nt long for the non treated cells. Hence we trimmed all reads to 76nt. MCF-7 dataset. MCF-7 were transfected (two biological replicates) with siRNA targeting both DDX5 and DDX17 RNA helicases, and total RNA were extracted as described previously  36. cDNA synthesis was made using the TruSeq Stranded Total RNA protocol after Ribo-Zero Gold-mediated elimination of ribosomal RNA (Beckman Coulter Genomics).
High throughput sequencing (2 × 125 bp) was carried out on an Illumina HiSeq 2500 platform (Beckman Coulter Genomics), generating between 45 and 50 millions of paired-end pairs of reads. Raw datasets are available on GEO under the accession number GSE94372. Reads were trimmed according to standard quality control filters using prinseq  37  and adapter were removed using cutadapt  38. The resulting reads had length between 25 and 125nt. Because MISO is unable to deal with reads of unequal length, we selected only reads with length larger than 100nt (87% of the reads) and trimmed longer reads to 100nt.
Acknowledgements This work was performed on the computing facilities of the computing center  LBBE/PRABI  and the  PSMN (Pole Scientifique de Modelisation Numerique) computing center of ENS de Lyon. This work was funded by the  ANR-12-BS02-0008  (Colib'read) by the  ABS4NGS   ANR  project ( ANR-11-BINF-0001-06 ), Action n3. 6 Plan Cancer 2009-2013,  Fondation ARC  ( Programme Labellisé Fondation   ARC 2014 ,  PGA120140200853 ) and  INCa  ( 2014-154 ). Doctoral fellowships from  ARC 1 -Région Rhône-Alpes  (C. B. P),  Science Without Borders -CNPq -Brazil  (L. L. -grant process number  203362/2014-4 ),  ARS RhÃ´ne-Alpes  (A. R. ) and post-doctoral fellowships from  Fondation ARC  (M. P. L).
The pellet was resuspended in PBS, incubated under agitation for 1 h at 4°C, centrifuged 1 min at 5000 rpm and 4°C, and aliquoted at -80°C. For virus titration, serial dilutions of supernatants had been tested on HCT116 cells, which were then analyzed for mkate2 detection by cytometry (BD Biosciences, Le-Pont-De-Claix, France), 4/5 days postinfection. All seven cell lines were plated at 1 × 10 5 cells per well in a 6-well plate and infected with viral supernatant with a high MOI. After cells reached confluency, a selection of the cells marked with Luc/mKate2 was performed by flow cytometry using FACSDiva version 6. 1. 3. software (BD Biosciences). The cells expressing the transgene were amplified for further use. Expression and activity were measured by bioluminescence using IVIS SpectrumCT system (Perkin Elmer, Courtaboeuf, France).
3A ), but did not detect lung or any other metastases. Saos-2-B-Luc/mKate2-CDX scans revealed tumor-bearing tibia bone structure abnormalities similar to those observed in the human disease. Aggressive bone lesions (cortical rupture, periosteal reaction), detection of aberrant new bone formation extending within the extraosseous mass (osteocondensation, new calcified material), and some osteolysis (bone destruction) were found as shown in Fig. 3A  when mice were sacrificed at day 67 and 77 for paratibial and intratibial, respectively. Osteocondensation was also observed inside the bone of intratibial models, but less in paratibial models (Fig. 3A ). These changes were first noted 41 days after Saos-2-B-Luc/mKate2 injection in NSG mice and 78 days in nude mice, independently of injection localization (data not shown).
AN EXEMPLARY BIOMEDICAL DATABANK Compared with many areas of biomedical science, visualization methods for macromolecular structures are more advanced, largely because they build upon a solid bedrock of exceptionally well managed data. Created in 1972, the Protein Data Bank (PDB; 51) has exemplary practices and stability that facilitate reproducibility and substantially simplify the difficult task of creating and maintaining tailored visualization tools. In particular, (a) each entry is a deposition related to one specific scientific publication, not to an abstract concept (e. g. , a gene or pathway) whose definition may change over time. (b) Entry identifiers are short and designed to be easy to remember. Depositors can propose an identifier, ensuring that many are meaningful. For example, the first crystal structure of the protein actin has identifier "1ATN"  (114).
(c) Entries include rich metadata describing how they were generated (and by whom), as well as cross-linking to related databases. (d ) Raw and processed data are stored, enabling later reanalyses. (e) An international network of organizations maintains and curates the database. ( f ) PDB deposition is required when publishing in major journals. Unfortunately, most biomedical databanks created since have not learned from these practices, thus requiring tool developers to contend with data formats and sources that are many, varied, and often unstable. indicate which parts of an organism's genome are active. To understand the biological role each gene plays, we must look beyond sequencing data; in fact, much of our current understanding has come from studying the molecular structure of RNA transcripts and the proteins they encode.
Protein Structures Protein structural biology aims to provide a detailed understanding of life's molecular machinery. Thanks to decades of research worldwide, we now have 3D molecular structures (at or near atomic resolution) for ∼40,000 proteins. By viewing these structures, researchers can gain insight into precise molecular mechanisms underlying many of the biochemical processes occurring within living cells. Remarkably, almost all these structures are collected in a single, exemplary database (https://www. wwpdb. org; 51) (see the sidebar titled An Exemplary Biomedical Databank). This has helped drive innovation in molecular graphics, which has outpaced visualization advances in many other areas of biomedical science  (22). Visualization challenges. Visualization is integral to structure determination and validation  (22) , as well as for gaining insight into protein function (e.
Thus, many tailored visualizations have already been developed  (23, 30) , and unfortunately, systems biology data are fragmented across ∼700 resources (http://pathguide. org; 73), which partly impedes progress in the field. To help manage the many complexities of these data, researchers use a wide range of visual techniques; for example, the overview/detail strategy is used to allow subgraphs to be collapsed or expanded upon demand, thus helping users more effectively explore large graphs. However, there remains considerable scope for using visualization principles to design new, automated layouts  (81, 82)  and to improve the computer-aided design of manual layouts. There is also considerable scope for improving how systems biology data are organized (see the sidebar titled An Exemplary Biomedical Databank).
Visualization challenges. Imaging data can give quantitative and qualitative insights into morphology and function; however, this often requires extensive and sophisticated processing pipelines  (89). An increasing array of tools are being developed to address this need, some of which now allow interactive visualization of raw images together with image-derived data, e. g. , the Medical Imaging Interactive Toolkit (http://mitk. org/) and Slicer (http://www. slicer. org/). The primary way that we interpret these image-derived data is by encoding them as colors and annotations that are then overlaid onto the original images. This presents a challenge because biological images are usually already very complex; thus, detail often needs to be removed from the original image (for example, by transforming the image's dynamic range or color space) before derived data can be overlaid for either data exploration or publication.
It can also be useful to exchange experiences with peers facing similar challenges. The annual Visualizing Biological Data (VIZBI) conference provides a forum for this exchange and provides a free online collection of videos and posters from previous meetings (http://vizbi. org/). The VIZBI forum is also designed to help bioinformaticians connect with graphic designers, graphic artists, and biomedical communicators using illustration or animation. It can also help to connect with computer scientists researching data visualization; as well as advising on good principles and practice, they can be valuable collaborators. A forum for such engagement is provided by the annual BioVis Symposium (http://biovis. net/). Tailored visualization tools play a critical role in research, some becoming widely used and highly cited  (102).
There are also common outstanding challenges, such as uncertainty visualization (104) and multiscale navigation. Unfortunately, some poor visualization practices are also common (e. g. , overly cluttered visualizations). However, there are good indicators that this situation is improving and that there is increased awareness of the importance of data visualization in the life sciences  (105). This is evidenced by the increased focus on visualization in mainstream conferences, as well as the emergence of more specialist meetings, such as VIZBI and BioVis. As we seek to improve our tailored visualizations, another common challenge is how to objectively assess the quality of a particular visualization method or tool  (106). An obvious and important quality measurement is rate of adoption by the community; however, the popularity of a visualization strategy often has more to do with the cognitive load required to first learn how to read it, rather than how effectively or expressively it allows data to be understood and new insights to be generated.
ACKNOWLEDGMENTS Much of this review emerged from discussions at VIZBI 2017 in Sydney, Australia. We are grateful for the many colleagues who contributed, helping articulate the problems of specific areas in the biosciences, and to  Bang Wong ,  Tamara Munzner , and  Jeffery Heer , who have developed groundbreaking best practices. We are grateful for funding organizations that have supported the VIZBI meeting series, especially  NIH ,  EMBO , the  University of Sydney , and the  Garvan Institute of Medical Research. We would also like to acknowledge useful discussion and comments with  Drew Berry ,  Christian Stolte ,  David James , and  Sean Humphrey.
Methods Data and code availability. The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. The computer code for this project is regularly updated for further research; the most recent version may be obtained from the corresponding author. Model overview. We used a generalized individual-based model to extend our eco-evolutionary framework beyond classic two-level predator-prey models to simulate a four trophic-level community in which the top or penultimate trophic levels (referred to as the top predator and secondary consumer, respectively) were subjected to a range of harvest intensities. We simulated evolution and ecology dynamically along a competition-defense tradeoff axis separately in each trophic level below the harvested level by allowing genotypes and phenotypes at that focal level to undergo selection based on the reciprocally interacting abundance dynamics of their own predators and prey.
Multi-trophic evolution. We subsequently tested for effects of evolution in multiple non-harvested trophic levels on the harvested species. Using our four-trophic level model in which the top predator was harvested, we allowed pairs of trophic levels to evolve to examine the potential for evolutionary reinforcement or compensation at multiple trophic levels. We also ran models in which all trophic levels below the harvested species evolved and varied food web length (four or three trophic levels total) to examine how "fishing down the food web"  19  would change the net effect of evolution on the harvested species. Model design and details. We built an individual-based model framework using Matlab R2015b software. The model uses iterative Monte Carlo methods to simulate four discrete populations, with each population constituting an entire trophic level (Fig.
1 ). Representing populations, genotypes, and phenotypes. Each population was represented by a data table, in which each row represented an individual and each column represented a trait. Births were appended to each table; deaths were deleted. Each individual in each population had a quantitative trait genotype influencing its competition-defense phenotype. Our assumption of polygenic inheritance is grounded in the premise that such performance phenotypes likely reflect the additive influence of many interacting traits that may or may not be polygenic themselves. For efficiency and generalizability, we produced subsequent generations by sampling directly from standing genetic variation (i. e. asexually) and applying a mutational component. Sampling to produce offspring was weighted by the survival and reproductive probabilities of current organisms.
Acknowledgements This project was supported by the  USDA National Institute of Food and Agriculture ,  Hatch  project number  ME0-31706  through the  Maine Agricultural & Forest Experiment Station , publication number 3592. Other funding sources include:  University of Maine Graduate Student Government ;  US National Science Foundation   DEB 1457112  and  DEB 1457333 ; and  NOAA Cooperative Institute for Marine Ecosystems and Climate. The authors thank  Tim Waring  [ University of Maine ] and  Ariel Wertheim  [ Dartmouth College ] for advice on model management, as well as  Steve Munch  [ NOAA ] and  Anna Kuparinen  [ University of Helsinki ] for manuscript comments and advice. The  University of Maine Ecology and Evolution of Everything group  also provided invaluable comments during the early stages of this project.
Objective: The objective of this study will be to identify and analyze the communication strategies through which remote consultations are accomplished and to guide patients and clinicians to improve the communicative quality of remote consultations. Methods: In previous research, we collected and analyzed two separate datasets of remote consultations in a National Institute for Health Research-funded study of clinics in East London using Skype and a Wellcome Trust-funded study of specialist community heart failure teams in Oxford using Skype or FaceTime. The Qualitative Analysis of Remote Consultations (QuARC) study will combine datasets and undertake detailed interactional microanalysis of up to 40 remote consultations undertaken by senior and junior doctors and nurse specialists, including consultations with adults with diabetes, women who have diabetes during pregnancy, people consulting for postoperative cancer surgery and community-based patients having routine heart failure reviews along with up to 25 comparable face-to-face consultations.
We have also observed heart failure nurses successfully talking patients and/or relatives through self-examination of ankle edema and blood oxygen and blood pressure testing. The combined dataset obtained from VOCAL and OTQS recordings offers opportunities for addressing questions about communication and quality of care in remote consultations. Preliminary analysis of the videos and transcripts across both studies suggests that remote consultations have advantages (eg, patients generally feel satisfied and many prefer consulting from the comfort of their own homes with family around them; clinicians who regularly use Skype or similar media are keen on this medium) but that they are different (eg, compared with the equivalent face-to-face encounter the overall length is shorter), even when taking account of the small amount of "technical talk" at the beginning (eg, "can you see me?" or "is the video on?"), and the flow of conversation is less natural  [18].
Objectives Our objectives are as follows: 1. To analyze a multimodal dataset of up to 40 remote consultations with patients diagnosed with diabetes, cancer, and heart failure, and their clinicians (and compare these with a subset of up to 25 audio-recorded face-to-face consultations) using a combination of ethnographic and microanalytic approaches to investigate, in detail, how interactions are affected by mediation via Skype or similar applications 2. To generate findings on the detailed dynamics of communication and interactions in remote consultations and bring patients and clinicians, who have been involved in remote consultations together for a consolidating learning workshop to gather feedback and develop/refine resources 3. To develop provisional guidance for patients and clinicians on conducting remote consultations (provisional in the sense that study design does not allow conclusions to be drawn across all clinical areas).