dataesr/scientific-paragraphs-categorization huggingface dataset:
Fig. S7 ). The main purpose of the MFGA is to enable researchers and clinicians to consider their own research results in the context of other relevant literature. To this end, the system enables a highly selective search for studies with comparable conditions and parameters and offers the means to quickly review their main analysis results. Additionally, searches for genes can be performed in order to identify data sets containing the corresponding genes in their analysis results. This functionality supports the validation of candidate genes. Further, supplementary information from various sources is embedded into the MFGA, e. g. , RefSeq (O'  Leary et al. , 2016) , HGNC  (Yates et al. , 2017; HGNC Database, 2018)  and GTEx project  (Lonsdale et al. , 2013;  The Broad Institute of  MIT and Harvard, 2019).
Compared to general search engines like Google Scholar and PubMed, with millions of records, the MFGA will always return smaller numbers of search results. However, in the domain of male infertility, the relevance of the individual results in relation to the corresponding search terms used in the MFGA is expected to be greater than when using those same search terms in a large search engine. Search engines for literature are usually restricted to mining textual information of publications and cannot consider information that is presented in tables, images, or supplementary material. The MFGA, however, is specifically designed for that task, which is enabled by its key features: A standardised data representation and disease-specific manual curation. Another problem occurs when relevant data sets are searched in raw data repositories like GEO or processed data resources like RGV  (Darde et al.
perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted February 13, 2020. ; https://doi. org/10. 1101 /2020. 02. 10. 20021790 doi: medRxiv preprint. 02. 10. 20021790 doi: medRxiv preprint  Male Fertility Gene Atlas (MFGA) 14 search and interpretation of OMICS data on male infertility and germ cells in the context of relevant publications. Moreover, its capacity for aggregation allows for meta-analyses and data mining with the potential to reveal novel insights into male infertility based on available data. Ultimately, by combining RGV and MFGA with AI methods, we aim to develop a powerful gene prioritisation system dedicated to male infertility similar to the GPSy tool  (Britto et al.
, 2012). (Chinosi and Trombetta, 2012). The process is triggered monthly. Potential publications are collected and merged into a list. This list is then prioritised by the MFGA team. Subsequently, curation and upload are performed in the determined order. represented in an interactive way and can directly be filtered and sorted. Additionally, histograms can be plotted for the table columns, here, e. g. , based on column "Testis Expression". For the full screenshot see Suppl. et al. , 2016)  and HGNC  (Yates et al. , 2017). Additionally, gene expression in different tissues is shown based on the data from the GTEx project  (Lonsdale et al. , 2013). Links for searching the gene in the MFGA database and opening the corresponding GeneCards entry  (Stelzer et al. , 2016)  are provided. Retrieved 10 January 2020.
Acknowledgements We are grateful to all members of the  Clinical Research Unit (CRU) 'Male Germ Cells ' who contributed through either identifying relevant publications, supporting publication curation or providing feedback during the development of the MFGA:  Michael Storck ,  Marius Wöste ,  Nina Neuhaus ,  Sven Berres ,  Sandra Laurentino ,  Lina Franziska Lanuza Pérez ,  Corinna Friedrich ,  Maria Schubert ,  Nikithomas Loges ,  Isabella Aprea ,  Jana Emich ,  Eva Maria Mall ,  Johanna Raidt ,  Nadja Rotte ,  Alexander Busch ,  Sara Kim Plutta ,  Jascha Henseler ,  Anna Natrup. We thank  Dr. Celeste Brennecka  for language editing of the manuscript.
They come to the conclusions that extracellular hemoglobin may exist in many more bilaterian groups than previously thought and that the last common ancestor of bilaterians may have possessed this hemoglobin. Despite bringing forward interesting conceptions, these previous works on metazoan globins all suffer from biased sampling. Gene sequences are identified from animals whose complete genome was not available at the time of the study and therefore represent only a subset of the diversity that must exist in metazoans. The sampling of globins of Blank and Burmester  (16)  was made by searching NCBI Genbank with terms related to globins, while the same database was screened with a Ngb probe in Lechauve et al  (18). Belato et al's study  (19)  focuses only on extracellular globins and uses only transcriptomes.
The animal globin superfamily has an ancient history independent from the history of blood The existence of these clades clearly indicates that at least five globin genes existed in Urbilateria. We call these five hypothetical molecules the "bilaterian stem-globins". Few globin sequences fall outside of these five well-defined clades (figure  6B ). These additional globins fall into two cases. In one case, these sequences may derive from the stem globins but could have evolved rapidly so as to blur sequence similarities. This is what we would suppose for the large clade of C. elegans globins found alongside a single clade I globin. The second case concerns the clade of sea anemone globins, which includes almost all the sea anemone globins in our dataset (except from a single clade V globin).
A novel scenario for the evolution of blood globins Oxyphoric circulatory globins have evolved several times during the course of metazoan evolution. All circulatory multigenic families in our dataset derive from a likely unique clade I globin, possibly after recruitment for the circulatory function and several rounds of gene duplications. These multigenic families encompass for example the human hemoglobin family as well as the extracellular globin families of annelids and Daphnia. In the case of the hemoglobins of vertebrate erythrocytes (RBC), heterotetramers comprising alternate globin isotypes circulate depending on the development stage. Adult human hemoglobins form heterotetramers comprising sub-units of two isotypes,  and , coded by paralogous genes  (44). During the course of human embryogenesis and foetal development, different isotypes of hemoglobins are produced (, , , ), coded by other paralogous genes located in two clusters of  and -related paralogues.
Survey of Platynereis dumerilii globin genes Platynereis dumerilii globin genes were identified with consecutive steps of sequence similarity searches. We first used a concatenated probe of all 12 distinct human globin sequences (Ngb + Mb + Cygb + Hb-2, -, -, -, -, -, -1, -2, -) as query against expressed sequence tags (ESTs) from Platynereis Resources (4dx. embl. de/platy/), and Jékely lab transcriptome (http://jekely-lab. tuebingen. mpg. de/blast/#). Complete coding sequences were assembled from EST fragments using CodonCode Aligner (CodonCode Corporation, USA) and consensus sequences were determined due to high level of polymorphism in this species. The sequences were reciprocally blasted against Genbank non redundant protein database. This allowed constituting a first list of sequences that were recognized as bona fide globins.
Survey of globin genes in animal genomes Gene searches were carried out using the tblastn or blastp algorithms implemented in ngKlast (Korilog V 4. 0, Questembert, France). We used a concatenated probe of all 17 Platynereis globins with distinct sequences as a query (Pdu Egb-A1a, -A1b, -A1c, -A1d-, -A2, -B1, -B2 + Pdu-gb-IIA, -IIB, -IIC, -IID, -IIIA, -IIIB, -IVA, -IVB + Pdu-Sp-gb + Pdu-Ngb). The sequences were reciprocally blasted against Genbank non redundant protein database. This concatenated query sequence covers as much as the molecular diversity of globins as possible and even very derived sequences were recovered in the hit lists. To ensure that all sequences with actual globin similarity are recovered, we reciprocally blasted hits with decreasing E-value until at least ten consecutive non-globin hits turn up in the list.
We used publicly available files of peptide predictions from 22 genome datasets widely distributed among the metazoan phyla: Porifera, Cnidaria, Placozoa, Ecdysozoa, Lophotrochozoa and Deuterostomia (additional file 13). When available, the screen was complemented with publicly available transcriptomes that allowed for the correction of a few annotation problems. In addition, the globin genes search was performed on the transcriptomes of Lumbricus terrestris, Arenicola marina and Alvinella pompejana. The putative extracellular globin genes were identified using Phobius signal peptide predictor (http://phobius. sbc. su. se/, Stockholm Bioinformatics Center) and SignalP-5. 0 predictor (http://www. cbs. dtu. dk/services/SignalP/) (additional file 6). The putative N-terminal acylation predictions were carried out using GPS-Lipid predictor (http://lipid.
biocuckoo. org/, 65) and GPS-Palm predictor (http://gpspalm. biocuckoo. cn/, 66) and compiled in additional file 6. The giant hemoglobin linker proteins were screened in all genomes in the same fashion as globins, using this time as a probe a concatenation of all linker proteins from the earthworm Glossoscolex found in Genbank. Predicted sequences of Pdu-L1, -L2a and -L2b were deposited in Genbank (MW075674-MW075676).
Cloning of extracellular globin genes and in situ probes design Large cDNA fragments, encompassing at least the whole coding sequences, were cloned by nested PCR using sequence-specific primers on cDNA from mixed larval stages and posterior regenerating segments. PCR products were cloned into the PCR2. 1 vector following the manufacturer's instructions (Invitrogen, France) and sequenced. The full list of primers used is provided in additional file 14. Sequences were deposited in Genbank (MT701024-MT701042). These plasmids were used as template to produce Dig RNA antisense probes for whole-mount in situ hybridization (WMISH) using Roche reagents.
Acknowledgements We warmly thank  Detlev Arendt's laboratory (EMBL Heidelberg, Germany) and Oleg Simakov's laboratory (University of Vienna, Austria)  for letting us use the data of the Platynereis whole genome sequencing and assembly ahead of publication. We thank the  Balavoine Lab Members and Biofluidics Lab members  for helpful discussions during the course of this work. We acknowledge  Nathalie Luciani  for equipment and training for qPCR experiments and analysis. We acknowledge the  ImagoSeine facility  (member of the  France BioImaging infrastructure  supported by the  French National Research Agency ,  ANR-10-INSB-04 , ' Investments of the future ') and its experienced staff for their assistance in photonic and electron microscopy imaging and image analysis. We acknowledge the facilities «Chromas» and «Center for Molecular and Cell Technologies» of  St.
Funding S. Song  obtained a PhD fellowship from the  LABEX "WHO AM I?"  (No. ANR-11-LABX-0071 ). The  Balavoine Lab  was funded by the  CNRS , the  Université de Paris  and two grants from the  ANR  ( METAMERE  no. ANR-12-BSV2-0021  and  TELOBLAST  no. ANR-16-CE91-0007 ).
The reporter cassette was directly followed by the kanMX selection cassette, which is made of a TEF1 promoter sequence (TEF1-prom), the G418-resistance sequence (G418 R ), and a TEF1 terminator (TEF1-term). The reporter + selection cassette was PCR amplified with oligonucleotides bearing homology to the C terminus of GAL1 and transformed into yeast. Transformants were selected on YPD plates supplemented with G418 and reporter insertions were tested by junction PCRs and validated by sequencing. Note: For lineage analyses, we recommend including a second fluorescent reporter to facilitate pedigree mapping. Although some segmentation algorithms, such as the custom software PhyloCell that we used for segmentation and lineage definition (see ''Custom software for cell-tracking microfluidics data processing'' and the Key resources table for details), are capable of reconstructing lineages without a lineage marker, using one reduces errors.
Inverted epifluorescence microscope Phase contrast and fluorescence images in  Bheda et al. (2020a)  were captured with Nikon NIS Elements software from an inverted Nikon Eclipse Ti-E epifluorescence microscope using a plan-apo l 1003/1. 45 NA Ph3 oil immersion objective, SPECTRA X light engine illumination, and an Andor iXon Ultra 888 EMCCD camera.
Synthetic complete (SC) media We recommend first preparing the media components by autoclaving the amino acid mix plus yeast nitrogen base together and separately sterile filtering the carbon source (sugar). To prepare media as in  Bheda et al. (2020a) : Note: autoclave on the same day that it is prepared  Note: prepare and sterile filter through a 0. 22 mm filter on the day of the experiment Custom software for cell-tracking microfluidics data processing Cells were segmented and mapped from frame to frame and nuclei were segmented to build pedigrees from the single-cell tracking microfluidics data in  Bheda et al. (2020a)  using the custom software PhyloCell and Autotrack (https://github. com/gcharvin) that operates in MATLAB. These software have been previously implemented for lineage analyses of S.
cerevisiae  (Bheda et al. , 2020a; Fehrmann et al. , 2013; Garmendia-Torres et al. , 2018; Goulev et al. , 2017; Paoletti et al. , 2016; Xu et al. , 2015). For help using these software, there are available files on Github as HTML web pages (https://github. com/gcharvin/phyloCell/help/html/index. html). In addition, there is a demo project that can be launched from PhyloCell to try out the features of the software (https://github. com/ gcharvin/phyloCell/demo).
Note: We recommend a flow rate of $300 mL/min for 20 min for each step, therefore running $6 mL of each liquid through the tubings. Cell-tracking microfluidics data processing This step describes how images collected from the single-cell tracking microfluidics data should be processed prior to quantification and statistical analyses. We have used the custom software Phylo-Cell (see ''Materials and equipment'' above for further details). 10. Segment cells using fluorescence or phase contrast images. Note: To use PhyloCell for this step, the images should first be imported to generate a segmentation project. We recommend JPEG image files. PhyloCell can generate a segmentation project in a number of ways. For more details, please access the help files available for Phylo-Cell on Github (see ''Loading images and generating segmentation projects with PhyloCell'').
Overview of the data Simple plots can be generated for an overall visualization of the microfluidics experiment results. 1. Organize data using the package pandas  (McKinney, 2010)  in Python which works with dataframe resulting in two-dimensional data structures with a tabular format. Pandas can be used to read tab separated files, among other file formats, into dataframes (e. g. , DataFrame = pandas. read_ table('input. tsv')). Using pandas, data can be easily grouped by a specified variable and this can Here we depict various possibilities A-D described in the text within the section on Quantification and statistical analysis. These analyses, and the sub-analyses listed beneath are not hierarchical, but can be applied independently of each other, based on the required analysis of the collected data.
We employed these analyses to dissect maintenance and inheritance of phenotypes during repeated inductions and found e. g. , asymmetric inheritance as depicted in mutant 1/condition 1 and gain-of-memory such as in mutant 2/condition 2. Please note, these analyses can be modified to address other important questions on single-cell inheritance in yeast and even other cell types. facilitate statistical analysis or plotting of data. For example, useful functions to do this in pandas are DataFrame. set_index() and DataFrame. groupby(). 2. Plot single-cell traces to observe the phenotype(s) (e. g. , fluorescence signal) over time  (Figure 4B, top). In Python, some useful packages for plotting are seaborn  (Waskom et al. , 2014)  and matplotlib  (Hunter, 2007). The lineplot function from seaborn (available from seaborn v0.
10. 0 onwards, previously tsplot()) allows to plot time series with error bands, confidence intervals, and data ranges, among many other features. An example using this function on microfluidics data over 200 data points, 3 different cell strains, and more than 450 cells, is available in the TimeSeriesPlot directory on Github (https://github. com/aguilar-gomez/SingleCellLineageAnalysis/). 3. Calculate the mean or the median of the phenotype(s). Include confidence intervals, standard deviation, or other measurements to represent the variation (Figure  4B , bottom). Useful packages to perform these statistical analyses in Python are scipy  (Virtanen et al. , 2020)  and numpy  (Harris et al. , 2020). 4. Plot pedigree of mother cells and their progeny according to a heatmap of the phenotype to observe the phenotype(s) over time and its potential inheritance (Figure  4C ).
This can be done in the PhyloCell GUI with Plot Pedigree/Parentage from the Analyze tab. Method A: Comparing strains/conditions/generations at single time points To compare single-cell phenotypes (e. g. , different strains, or the same strain at different time points or in different conditions, or mothers vs daughters), distributions at single time points of interest can be visualized. 1. Plot data for phenotype of interest grouped by e. g. , strain or condition using boxplots, violinplots, kernel densities, or histograms (Figures  6A-6C ). As mentioned above, the seaborn and matplotlib packages in Python are useful for data visualization, including the functions matplotlib. pyplot. boxplot() and seaborn. boxplot(). Examples using these functions are available on the provided Github link (https://github.
com/aguilar-gomez). An alternative package for plotting data using a language other than Python is ggplot2 in R  (Wickham, 2011). Note: Depending on the type of data some plots might be favored: Quantitative (numerical) data: histograms, boxplots, violinplots, and scatterplots might be preferred. Boxplots depict where most of the data lies, as the box represents the 25 th to 75 th percentiles. Violinplots are very similar to boxplots, but additionally they have a rotated kernel density on each side. Categorical (ordinal and nominal) data: bar charts and dot charts. The counts of each categorical data can be plotted with bar charts. Dot plots can be used to plot the difference in proportions per category. 2. Perform statistical tests to confirm actual differences between groups. Note: For more information on choosing statistical tests, refer to section on Assessing statistical significance.
Method B: Determining association of conditions/generations at single time points To determine whether two phenotypes that have been observed are related, the data for one phenotype can be plotted as a function/relative to the second phenotype (e. g. , different strains, or the same strain at different time points or in different conditions, or mothers vs daughters). 1. Plot the phenotypes as a scatter plot against each other, with one observation on the x axis and the other along the y axis (Figure  7A ). A function called matplotlib. pyplot. scatter() can be used to generate scatterplots in Python, or alternatively the geom_point() function within ggplot2 in R.
For example, a positive slope indicates a directly proportional relationship, which means that if one of the variables increases, the other one does as well. Linear regression using least squares can be performed in Python by importing numpy and using the function numpy. linalg. lstsq(), which takes x and y as parameters and returns the coefficients (including the offset) and the residuals. Relative difference at single time points 1. Calculate relative differences ðRDÞ as a measure for how similar or different two single-cell phenotypes are (Figure  7B ).
Method C: Comparing trajectories of cells over time With time-lapse data, correlations can be calculated over multiple time points, e. g. , to compare a single cell to itself during two inductions or using the lineage information for analyzing inheritance from pairs of mother and daughter cells. 1. As previously, plot the phenotypes as a scatter plot against each other, with one observation on the x axis and the other along the y axis, and now use a color scale for the time (Figure  8A ) or make a 3D plot where the z axis is time and each cell is plotted with a different color/symbol (Figure  8B ). These figures can be generated in MATLAB using the function for plotting in 3D axis scatter3D(); an example of this application is provided within scatter3d_Compare2intervals. m under the directory PHYLOCELLcomplenent on Github (https://github.
, 2020a ) is available on Github (https://github. com/aguilar-gomez/SingleCellLineageAnalysis) under the directory Partial Correlation which contains a jupyter notebook (PartialCorrelations. ipynb) with the function partial_corr() used to calculate partial correlations. This function takes three arguments which all must be vectors of the same size, from which two vectors are used for the correlation and a third used as a controlling variable. This analysis returns the Pearson correlation coefficient of the residuals. Coefficient of variation over time 1. Calculate coefficients of variation for all cells within the population at each time point to determine how the noise in expression in the population is changing over time (Figure  8E ). 2. For data with lineage information, calculate coefficients of variation between sublineages of cells ðSLCV Þ.
Then, x i is the average phenotype among cells of a sublineage and m s is the average across all x i. 3. Bootstrap cells in each time point to create a distribution of CVs and to calculate error bars by the standard error of the mean (SEM) or the standard deviation (STD). Note: A Python notebook is available on Github with an example on how to perform this analysis (https://github. com/aguilar-gomez/SingleCellLineageAnalysis/blob/master/Sublineage_ CV/SLCV. ipynb).
Binomial test 1. Perform a binomial test as a simple method to determine potential asymmetry of memory inheritance. Transform the data into a categorical variable by counting the cases k out of the total number of pairs of related cells N (mother-daughter relationships) where the mother has higher/lower values than its daughter. Then, a binomial test can be performed to test the probability ðPÞ that the trait is symmetrically inherited ðp = 50%Þ or higher in mothers or daughters. P = N! k!ðN À kÞ! p k À 1 À p Á nÀk Note: R base has the function biom. test(k,N,p) to perform the test. An example of this analysis is provided on Github (https://github. com/aguilar-gomez/SingleCellLineageAnalysis) within the script AsymmetricDivision. R.
Figure5. Overview of possible options for single-cell lineage-tracking analysis (A) Cell-tracking microfluidics data can be quantified and statistically analyzed in myriad ways. Here we depict various possibilities A-D described in the text within the section on Quantification and statistical analysis. These analyses, and the sub-analyses listed beneath are not hierarchical, but can be applied independently of each other, based on the required analysis of the collected data. (B) The strength of the cell-tracking microfluidics setup implemented is the ability to dissect transmission of phenotypes into maintenance in mother cells (M) and inheritance into their specific daughter cells (D). Maintenance is measured by analyzing differences between a mother cell at an initial time point (M1) and the same cell at a later time point (M2) (top panel).