dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatics The MGI database (http://www. informatics. jax. org/) was used to find information about SNPs and strain polymorphisms occurring within the Chromosome 7 interval  (queried -June 20, 2016). This database now includes variants detected in the sequencing of 17 mouse genomes  (Keane et al. , 2011). The 3 SNPs located at the Chromosome 7 QTL peak (rs13479451, rs3724540, rs6386601) are at ~114. 5 Mb in GeneNetwork and ~107. 6 Mb in the MGI and NCBI databases (http://www. ncbi. nlm. nih. gov/gene). In GeneNetwork, the relevant QTL interval on Chromosome 7 was 110-125 Mb whereas this region corresponds to 103-118 Mb in the MGI database. As finer mapping methods are developed, the exact interval location as reported here, may change. DAVID version 6. 8 (https://david. ncifcrf. gov/) was used to obtain functional annotations and pathway information for the genes within the interval and GeneWeaver 1.
A suggestive QTL for the rearing trait data was already apparent on Chromosome 7 from the whole genome scan, but this was not the case for the locomotion trait data  (Philip et al. , 2010). The area under the QTL peak contains 280 genes (MGI Chromosome 7: 103-118 Mb, GeneNetwork Chromosome 7: 110-125 Mb; database query-June 20, 2016, Supplementary worksheet 2). Of these genes, 162 are associated with olfactory receptors. Of the other genes with functional annotations, 11 are connected with neural processes (Adm: neural tube development, Appb1, Tub, Calca, Cckbr, Cnag4: sensory perception and cognition, Insc, Arntl: neurogenesis, Tpp1: neuromuscular control,.
REVISED Rras2: regulation of neuron death and Pde3b: morphine addiction (KEGG pathway 05032). There are 3 SNPs mapped at the location of the LRS peak and two positional candidates: Synaptogamin 9 (Syt9: rs13479451, rs3724540) and PPFIA binding protein 2 (Ppfibp2, rs6386601). Both of these genes differ between the parental strains (MGI database query -June 20, 2016). Syt9 functions in vesicle traffic and Ca2+ triggered exocytosis (Ncbi, 2016b) and Ppfibp2 is involved in the regulation and development of neuronal synapses (Ncbi, 2016a). These two candidates are also differentially expressed in the striatum of the brains of mice from strains with distinct opioid sensitivity  (Korostynski et al. , 2006; GeneWeaver: Syt 9, geneset #86830; Ppfibp2, geneset #86906).
Conclusions Genetic analysis of the BXD RIS resulted in the detection of an additional locus for morphine induced locomotor activity and rearing on Chromosome 7 which may be associated with activation of the reward system pathway in response to morphine treatment. Whether this effect is opioid-specific or also occurs with other classes of drugs is not yet clear. Alternatively, this QTL may also be related to general locomotor activity or nociception, a central nervous system response to potentially harmful stimuli. The best gene candidates within the QTL interval, besides the positional candidates Syt9 and Ppfibp2, are Calca and Cckbr (sensory perception of pain) and Pde3b (morphine addiction). F1000Research 1.
afforded by both GeneNetwork and the BXD recombinant inbred mice. Specifically, they used composite interval mapping to reanalyze publicly available phenotypic and genotypic data in the BXDs to identify a QTL on chromosome 7 that had been masked by a major QTL on chromosome 10. Next, they employed a bioinformatics approach to prioritize among candidate genes within the QTL interval. This resulted in the identification of promising candidate genes that may be associated with drug-abuse phenotypes. One step that would strengthen the manuscript would be to use QTLminer in GeneNetwork to examine cis -regulation of the genes located within the QTL support interval (or even just the most promising candidate genes) and to see if any of those genes were expression QTLs in BXD mice. In addition, the authors raise an interesting question when they ask whether the chromosome 7 QTL is opioid-specific, or if it also occurs with other classes of drugs.
have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. No competing interests were disclosed. of Psychology and Program in Neuroscience, Middlebury College, Middlebury, VT, USA The manuscript from Crusio. describes the use of GeneNetwork to identify QTLs associated with et al morphine response in a genetic reference mouse population. The authors highlight many strengths afforded by both GeneNetwork and the BXD recombinant inbred mice. Specifically, they used composite F1000Research.
Data availability All data are available in GeneNetwork (www. genenetwork. org; see supplementary worksheet 1 for trait-ids and descriptions). Trait IDs: Locomotion: 11843, 11833, 11844, 11834, 11845, 11835,  11846, 11847, 11851, 11852, 11836, 11837, 11838, Rearing:  11884, 11885, 11886, 11887, 11888, 11878, 11854. Data are also available in the Supplementary material.
I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. No competing interests were disclosed. of Psychology and Program in Neuroscience, Middlebury College, Middlebury, VT, USA The manuscript from Crusio. describes the use of GeneNetwork to identify QTLs associated with et al morphine response in a genetic reference mouse population. The authors highlight many strengths afforded by both GeneNetwork and the BXD recombinant inbred mice. Specifically, they used composite F1000Research.
, 2013)  giving a final fragment of 296 bp to amplify the microsatellite area and (2) C2-C2rev  (Lynch et al. , 2013)  giving a fragment of 400 bp to identify the characteristic polymorphism of Irish OsHV-1 µvar variant. These sequences overlapped and were assembled to obtain a longer fragment for homology searches. PCR products were cleaned up using QIAquick gel extraction kit (Qiagen) and sequenced by the Sanger method using the corresponding primers pairs used for amplification, (SourceBioScience). Raw chromatograms were analyzed using Chromas 231 software (Technelysium). ExPaSy tools (http://us. expasy. org/genomics) and CAP3 (http://doua. prabi. fr/software/cap3) were used for sequence assembly. Multiple sequence alignment were executed by Muscle  (Edgar, 2004)  and visualized and edited using BioEdit v.
Monitoring autophagy by following steady state amounts of LC3-II without including inhibitors in the analysis can result in an incorrect interpretation that autophagy is not taking place (due to the apparent absence of LC3-II). Conversely, if there are high levels of LC3-II but there is no change in the presence of inhibitors, this may indicate that induction has occurred but that the final steps of autophagy are blocked, resulting in stabilization of this protein. This figure was modified from data previously published in ref. 26, and is reproduced by permission of Landes Bioscience, copyright 2007. (B) Lysates of 4 human adipose tissue biopsies were resolved on 2-12% polyacrylamide gels, as described previously. 217  Proteins were transferred in parallel to either a PVDF or a nitrocellulose membrane, and blotted with anti-LC3 antibody, and then identified by reacting the membranes with an HRP-conjugated anti-rabbit IgG antibody, followed by ECL.
The LC3-II/LC3-I ratio was calculated based on densitometry analysis of both bands. Ã , P < 0. 05. (C) HEK 293 and HeLa cells were cultured in nutrient-rich medium (DMEM containing 10% fetal calf serum) or incubated for 4 h in starvation conditions (Krebs-Ringer medium) in the absence (¡) or presence (C) of E-64d and pepstatin at 10 mg/ml each (Inhibitors). Cells were then lysed and the proteins resolved by SDS-PAGE. Endogenous LC3 was detected by immunoblotting. Positions of LC3-I and LC3-II are indicated. In the absence of lysosomal protease inhibitors, starvation results in a modest increase (HEK 293 cells) or even a decrease (HeLa cells) in the amount of LC3-II. The use of inhibitors reveals that this apparent decrease is due to lysosomedependent degradation. This figure was modified from data previously published in ref.
This latter point is critical because, even in nutrient-rich conditions, cells display some basal level of GFP-LC3 puncta. There are, however, practical issues with counting puncta manually and reliably, especially if there are large numbers per cell. Nevertheless, manual scoring may be more accurate than relying on a software program, in which case it is important to ensure that only appropriate puncta are being counted (applicable programs include ImageJ, Imaris, and the open-source software CellProfiler 303 ). Moreover, when autophagosome-lysosome fusion is blocked, larger autophagosomes are detected, possibly due to autophagosomeautophagosome fusion, or to an inability to resolve individual autophagosomes when they are present in large numbers. Although it is possible to detect changes in the size of GFP-Atg8/LC3 puncta by fluorescence microscopy, it is not possible to correlate size with autophagy activity without additional assay methods.
105  In general, when working with yeast it is preferable to use strains that have the marker proteins integrated into the chromosome rather than relying on plasmid-based expression, because plasmid numbers can vary from cell to cell. The GFP-Atg8, or similar, processing assay is easy to perform and is suitable for analysis by microscopy as well as western blotting; however, particular care is needed to obtain quantitative data for GFP-Atg8, Pex14-GFP or Om45-GFP, etc. processing assays (see cautionary notes for GFP-Atg8/LC3 lysosomal delivery and proteolysis). An alternative is an organelle-targeted Pho8D60 assay. For example, mitoPho8D60 can be used to quantitatively measure mitophagy. 686  In addition, for the GFP-Atg8 processing assay, 2 h of starvation is generally sufficient to detect a significant level of free (i.
Nomenclature To minimize confusion regarding nomenclature, we make the following notes: In general, we follow the conventions established by the nomenclature committees for each model organism whenever appropriate guidelines are available, and briefly summarize the information here using "ATG1" as an example for yeast and mammals. The standard nomenclature of autophagy-related wild-type genes, mutants and proteins for yeast is ATG1, atg1 (or atg1D in the case of deletions) and Atg1, respectively, according to the guidelines adopted by the Saccharomyces Genome Database (http://www. yeastgenome. org/gene_guidelines. shtml). For mammals we follow the recommendations of the International Committee on Standardized Genetic Nomenclature for Mice (http://www. informatics. jax. org/mgihome/nomen/), which dictates the designations Atg1, atg1 and ATG1 (for all rodents), respectively, and the guidelines for human genes established by the HUGO Nomenclature Committee (http://www.
Nonetheless, the expressed sequence transcript databases facilitate the design of targeting constructs. For steady-state measurement, autophagy can be monitored by western blot or by immunofluorescence using antibodies to ATG8/LC3. 1425  Flux measurements can be made in a trout cell culture model (for example, in primary culture of trout myocytes) by following ATG8/LC3 turnover in the absence and presence of bafilomycin A 1. It is also possible to monitor the mRNA levels of ATG genes by real-time PCR using primer sequences chosen from trout sequences available in the abovementioned expressed sequence transcript database. A major challenge in the near future will be to develop for this model the use of RNAi-mediated gene silencing to analyze the role of some signaling proteins in the control of autophagy, and also the function of autophagy-related proteins in this species.
Other processes ATG proteins are involved in various other nonautophagic processes, particularly apoptosis and noncanonical protein secretion, as discussed in various papers. 27, 75, 76, 544, 572, 1449, [1461] [1462] [1463] [1464] [1465] 1466  E. Interpretation of in silico assays for monitoring autophagy The increasing availability of complete (or near complete) genomes for key species spanning the eukaryotic domain provides a unique opportunity for delineating the spread of autophagic machinery components in the eukaryotic world. 1467, 1468  Fast and sensitive sequence similarity search procedures are already available; an increasing number of experimental biologists are now comfortable "BLASTing" their favorite sequences against the ever-increasing sequence databases for identifying putative homologs in different species.
1475  In such cases, sophisticated (manual) iterative database search protocols, including proper handling of compositionally biased subsequences and considering domain architecture may assist in eliminating spurious similarities or in the identification of homologs that share low sequence identity with the search molecule. [1473] [1474] [1475]  Genome-aware comparative genomics methods 1476 can also provide invaluable information on yet unidentified components of autophagy. However, care should be taken to avoid possible Next Generation Sequencing artifacts (usually incorrect genome assemblies): these may directly (via a similarity to a protein encoded in an incorrectly assembled genomic region) or indirectly (via propagating erroneous annotations in databases) give misleading homolog assignments (V.
Web-based resources related to autophagy A number of autophagy-related resources are now available online, providing access to diverse data types ranging from gene lists and sequences to comprehensive catalogs of physical and indirect interactions. In the following we do not attempt to review all functionalities offered by the different servers, but to highlight those that (a) offer possibilities for identifying novel autophagy-related proteins or (b) characterize features that may link specific proteins to autophagic processes. Two comments regarding biological databases in general also apply to autophagy-related resources as well: (a) the need for regular updates, and (b) data and annotation quality. Nevertheless, these issues are not discussed further herein. a. The THANATOS database THANATOS (THe Apoptosis, Necrosis, AuTophagy Orches-tratorS) is a resource being developed by the CUCKOO Workgroup at the Huazhong University of Science and Technology (Wuhan, Hubei,China).
THANATOS is still under development (Y. Xue, personal communication) and it is focused on the integration of sequence data related to the main mechanisms leading to programmed cell death in eukaryotes. A simple web interface assists in data retrieval, using keyword searches, browsing by species and cell death type, performing BLAST searches with user-defined sequences, and by requesting the display of orthologs among predefined species. A Java application is also available to download for standalone usage of the THANATOS resource. The THANATOS database is publicly available online at the URL http://thanatos. biocuckoo. org/.
b. The Human Autophagy Database (HADb) The human autophagy database, developed in the Laboratory of Experimental Hemato-Oncology (Luxembourg), lists over 200 human genes/proteins related to autophagy. 604  These entries have been manually collected from the biomedical literature and other online resources 604 and there is currently no information that the initially published list has been further updated. For each gene there exists information on its sequence, transcripts and isoforms (including exon boundaries) as well as links to external resources. HADb provides basic search and browsing functionalities and is publicly available online at the URL http://autophagy. lu/.
c. The Autophagy Database The Autophagy Database is a multifaceted online resource providing information for proteins related to autophagy and their homologs across several eukaryotic species, with a focus on functional and structural data. 1477  It is developed by the National Institute of Genetics (Japan) under the Targeted Proteins Research Program of the Ministry of Education, Culture, Sports, Science and Technology (http://www. tanpaku. org/). This resource is regularly updated and as of August 2014 contained information regarding 312 reviewed protein entries; when additional data regarding orthologous/homologous proteins from more than 50 eukaryotes is considered, the total number of entries reaches approximately 9,000. In addition to the browse functionalities offered under the "Protein List" and the "Homologs" menus, an instance of the NCBI-BLAST software facilitates sequence-based queries against the database entries.
d. The Autophagy Regulatory Network (ARN) The most recent addition to the web-based resources relevant to autophagy research is the Autophagy Regulatory Network (ARN), developed at the E€ otv€ os Lor and University and Semmelweis University (Budapest, Hungary) in collaboration with the Institute of Food Research and The Genome Analysis Centre (Norfolk, UK). Maintanence and hosting the ARN resource is secured at The Genome Analysis Centre until at least 2019. ARN is an integrated systems-level resource aiming to collect and provide an interactive user interface enabling access to validated or predicted protein-protein, transcription factor-gene and miRNA-mRNA interactions related to autophagy in human. 1479  ARN contains data from 26 resources, including an in-house extensive manual curation, the dataset of the ChIP-MS study of  Behrends et al.
h. The ncRNA-associated cell death database (ncRDeathDB) The noncoding RNA (ncRNA)-associated cell death database (ncRDeathDB),  1485  most recently developed at the Harbin Medical University (Harbin, China) and Shantou University Medical College (Shantou, China), documents a total of more than 4,600 ncRNA-mediated programmed cell death entries. Compared to previous versions of the miRDeathDB,  [1486] [1487] [1488]  the ncRDeathDB further collected a large amount of published data describing the roles of diverse ncRNAs (including micro-RNA, long noncoding RNA/lncRNA and small nucleolar RNA/snoRNA) in programmed cell death for the purpose of archiving comprehensive ncRNA-associated cell death interactions. The current version of ncRDeathDB provides an all-inclusive bioinformatics resource on information detailing the ncRNA-mediated cell death system and documents 4,615 ncRNA-mediated programmed cell death entries (including 1,817 predicted entries) involving 12 species, as well as 2,403 apoptosis-associated entries, 2,205 autophagy-associated entries and 7 necrosis-associated entries.
Acknowledgments In a rapidly expanding and highly dynamic field such as autophagy, it is possible that some authors who should have been included on this article have been missed. D. J. K. extends his apologies to researchers in the field of autophagy who, due to oversight or any other reason, could not be included on this article. I also note that two of our colleagues on this manuscript have passed away:  Arlette Darfeuille-Michaud  and  Wouter van Doorn.
1181. Sequence comparison and comparative genomics approaches. 118  2. Web-based resources related to autophagy. 119 a. The THANATOS database. 119 b. The human autophagy database (HADb). 119 c. The Autophagy Database. 119 d. The Autophagy Regulatory Network (ARN). 119 e. Prediction of Atg8-family interacting proteins. 119 f. The iLIR server. 119 g. The Eukaryotic Linear Motif resource (ELM). 120 h. The ncRNA-associated cell death database (ncRDeathDB). 120 3. Dynamic and mathematical models of autophagy.
An XOR operation would not allow for this possibility. Test calculation with incomplete distance information. To get a better understanding of the strengths and shortcomings of our approach to model two-copy chromosomes, we ran test calculations on small protein structures that have the same size, but different topology. We used ubiquitin (PDB code 1ubq) and an NMR structure of the HRDC domain (PDB code 2ma1) as test systems. Ubiquitin is 76 amino acids in length, the HRDC domain is 75 amino acids long. By ignoring the last amino acid of ubiquitin, both structures can be represented by 75 beads located at the Cα positions. As a first test, we ran calculations with distance data. For both protein structures, we used all distances smaller than 10Å as input resulting in 547 distances for ubiquitin and 504 distances for the HRDC domain.
This shows that ISD is capable of drawing correct samples from the posterior distribution: Distance data alone cannot distinguish between a 3D structure and its mirror image. Therefore, we expect that the original structure of the Hilbert curve and its mirror image should be present in the ISD ensemble with identical probability. We also computed structures with sparsified versions of the full set of contacts by randomly selecting a smaller number of contacts. In the sparsified data sets, the number of contacts ranged from 3326 to 578 contacts corresponding to 2. 8% to 0. 44% of all distances (the sparsity of the contact data in case of the ISD bead model of the X chromosome at 500 kb resolution is 0. 68%). For all data sets, we obtained an ensemble of structures that comprised two major conformers: an approximate version of the input structure and its mirror image These tests show that (1) ISD is capable of generating statistically valid ensembles, which is indicated by the fact that we obtained a bimodal posterior ensemble, in which the peaks corresponding to the original structure and its mirror image have identical spread and population (see S12 Fig).
Software Simulations were performed using an extended version of the ISD library  [57]. Analyses were carried out with Python scripts that rely only on widely used additional packages and the CSB toolbox  [58]. Code and scripts for analysis are available at https://github. com/michaelhabeck/ isdhic. Computation times. Tests for the calculation of a single structure were done on a standard laptop with 2. 90GHz Core i7 8-core processor with 16 GB memory. At a resolution of 500 kb, the test consisted of 1000 steps of HMC starting from an extended structure. At a resolution 50 kb, 10000 steps of HMC were run starting from an extended structure. To compute the posterior ensemble of the X chromosome, we ran the replica-exchange algorithm on a computer cluster using 50 CPUs. At a resolution of 500 kb, we simulated 200 replica transitions where each transition consisted of 10 steps of HMC each using 250 leapfrog integration steps.
2013). Furthermore, data from the sequence-based typing (SBT) scheme (analogous to multilocus sequence typing), that allows a subdivision of L. pneumophila into sequence types (ST) based on the sequence of seven genetic loci (http://www. hpa-bioinformatics. org. uk/legionella/ legionella_sbt/php/sbt_homepage. php), revealed that a small subset of STs accounts for a disproportionately high number of clinical cases. In particular, five STs  (1, 23, 37, 47, and 62)  have accounted for nearly half of all epidemiologically unrelated LD cases in northwest Europe reported to the SBT database (Fig. 1A ; Supplemental Methods). There is no evidence that the high proportion of isolates found in clinical samples belonging to these five STs is a result of laboratory artifacts such as an increased growth of these STs in culture compared with other STs.