dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018). We used the 5 state hidden Markov model (HMM), representing background, initiation, pause, body, and after polyadenylation cleavage site decay from tunits. To improve sensitivity for transcription unit discovery in each tissue, the input to tunits was the output of dREG and bigWig files that were merged across all replicates and crosses. Scripts can be found here: https://github. com/Danko-Lab/F1_8Organs/blob/main/Tunit_predict_manuscript. sh https://github. com/Danko-Lab/F1_8Organs/blob/main/run. hmm. h5_F1bedgraph. R https://github. com/Danko-Lab/F1_8Organs/blob/main/hmm. prototypes. R.
Clustering: We used all transcripts that are 10,000 bp long from GENCODE vM25. Only reads mapped to the gene body (500bp downstream of the start of the annotation to the end of the annotation) were used. We filtered the transcripts and only kept those with at least 5 mapped reads in every sample. We export rpkm normalized expression estimates of each transcript. Morpheus was used to calculate and plot Spearman's rank correlation (https://software. broadinstitute. org/morpheus ) with the following parameters: Metric = Spearman rank correlation, Linkage method = Average Linkage, distance. function. name= Spearman rank correlation. Scripts can be found here: https://github. com/Danko-Lab/F1_8Organs/blob/main/getCounts_skipfirst500. R Allele specific heatmaps in Fig. 1  C focused on all genes that are longer than 10,000 bp from GENCODE vM25 and were allele specific in at least 3 of the samples from each organ.
1. As a control set, we used all transcription units regardless of allelic bias. For each transcription unit in the allele specific and control set, we identified the number with a FDR corrected binomial test for allele specificity <0. 1 (representing allele specific) or >0. 9 (representing confident non-allele specific). The differences between groups were tested using a Fisher's exact test. AlleleHMM: Maternal-and paternal-specific reads mapped using AlleleDB were used as input to AlleleHMM  (Chou and Danko, 2019). We combined biological replicates from the same organ and cross, and used the allele-specific read counts as input to AlleleHMM. AlleleHMM blocks were compared with GENOCODE gene annotations to pick the free parameter, τ, which maximized sensitivity and specificity for computing entire gene annotations, as described  (Chou and Danko, 2019).
Most organs used a τ of either 1E-5 (brain, liver, spleen, skeletal muscle) or 1E-4 (heart, large intestine, kidney, and stomach). As reported, the primary parameter that influenced τ was the library sequencing depth. AlleleHMM scripts can be found here: https://github. com/Danko-Lab/AlleleHMM https://github. com/Danko-Lab/F1_8Organs/blob/main/01_F1Ts_AlleleHMM. bsh Discovering strain effect and imprinted domains: We used the following rules to merge nearby allele specific transcription events into starin effect or imprinted domains: 1. Identify candidate AlleleHMM blocks using pooled ChRO-seq reads from samples with the same organ and same cross direction. 2. Combine blocks above from the same organ (but different crosses). Combine p-values using Fisher's method for all biological replicates within the same tissue and cross direction.
Keep blocks that are biased in the same direction with a Fisher's p-value <= 0. 05. 3. Determine whether the blocks are under a strain effect (allelic biased to the same strain in reciprocal crosses) or parent-of-origin imprinted effect (allelic biased to the same parent in reciprocal crosses). 4. Merge overlapping strain effect blocks from different organs into strain effect domains; merge overlapping strain effects from the same imprinted blocks into imprinted domains. Scripts implementing these rules can be found here: https://github. com/Danko-Lab/F1_8Organs/blob/main/Find_consistent_blocks_v3. bsh After discovering blocks, we examined the number of gene annotations in each domain (Fig. 1E ). We used GENCODE annotated genes (vM25). We kept all gene annotations and merged those which overlapped or bookended (directly adjacent to, as defined by bedTools) on the same strand so that they were counted once.
All operations were performed using bedTools  (Quinlan and Hall, 2010). Scripts can be found here: https://github. com/Danko-Lab/F1_8Organs/blob/main/Find_consistent_blocks_v3. bsh https://github. com/Danko-Lab/F1_8Organs/blob/main/Imprinted_figures. R Determining the allelic bias state of annotated genes: We used GENCODE gene annotations representing protein-coding genes (vM20) in which the transcription start site overlapped a site identified using dREG  (Wang et al. , 2018). We used de novo annotations by the tunits package to identify unannotated transcription units, which do not overlap an annotated, active gene as a source of candidate transcribed non-coding RNAs. Transcription units from both sources were merged for downstream analysis. We determine if the gene/ncRNA are allelic biased by comparing mapped reads to the B6 and CAST genomes using a binomial test, retaining transcription units with a 10% false discovery rate (FDR).
Evaluate the contribution of expression to organ-specific allelic biased domains (OSAB domain): In Supp Fig. 1C , we asked whether OSAB domains were not actively transcribed in candidate unbiased organs. Using bedtools and in-house scripts, we calculated the rpkm (Reads per kilobase per million mapped reads) normalized transcription level of each strain effect block located within the OSAB domains in each organ. The full diploid genome was used for mapping. The non-allelic-biased organs with highest rpkm (nonBiasedH) were selected to compare with the rpkm of the allelic-biased organs in OSAB domains. Scripts implementing this can be found here: https://github. com/Danko-Lab/F1_8Organs/blob/main/AllelicBiase_expressionLevel. bsh https://github. com/Danko-Lab/F1_8Organs/blob/main/getNonBiasedHighest_Biased_TotalReadCountRatio.
To test whether the candidate maxTSSs represented bona-fide transcription start sites, we generated sequence logos centered on the maxTSS using the seqLogo R package  (Bembom, 2019). We retained tissues in which the maxTSS contained a clearly defined initiator dinucleotide that reflects a similar sequence composition as those previously reported  (Tome et al. , 2018). Additionally, we used an in-house R script to examine the relationship between TSS counts and Read counts of the TSC (Supp Fig2 B), and found a similar relationship to those reported  (Tome et al. , 2018). Identify allele specific differences in TSCs abundance (ASTSC abundance): We used a binomial test to identify candidate allele specific transcription start clusters, with an expected allelic ratio of 0. 5. We filtered candidate allele specific differences using a false discovery rate (FDR) corrected p-value of 0.
Toward this end, we identified TSCs which do not overlap AlleleHMM blocks. We set the allele with high and low expression based on allele specific reads in the maxTSS. Next, we divided data into a test and background control dataset in which the test set had a CA dinucleotide in the allele with high maxTSS use and any other combination except for CA on the other allele. The control set did not have a CA dinucleotide in the maxTSS initiator position. Next we computed the distance to the maxTSS and the allelic read count at other candidate initiator motifs (including CA, CG, TA, TG). In all analyses, we compared the set of maxTSSs with SNPs in the initiator position with the control set which did not have a SNP. Statistical tests used an unpaired Wilcoxon rank sum test. We corrected for multiple hypothesis testing using false discovery rate.
5 and 2. Comparing GC content near the maxPause position: We compared the G, C, and GC content between alleles. We computed the G, C, and GC content as a function of position relative to maxPause. All of the G, C, and GC contents were combined across unique pause sites from all three organs for which we had single base resolution data (n = 3456). We compared three blocks: block 1 was 11 to 20 nt upstream of maxPause, block 2 was 1 to 10 nt upstream of maxPause, and block 3 was 1 to 10 nt downstream of maxPause. All scripts implementing analysis of allele-specific pause can be found at: https://github. com/Danko-Lab/F1_8Organs/tree/main/pause.
, 2021). The stability was defined as the ratio of RNA-seq read counts in exons to ChRO-seq read counts across the gene body. We used gene annotations from GENCODE (vM25). The RNA-seq reads were counted strand specifically using htseq-count. ChRO-seq reads were counted in a strand-specific fashion using in-house R scripts. After removing the genes with less than 10 B6-specific ChRO-seq and less than 10 CAST-specific ChRO-seq reads, the cumulative distribution functions were drawn. All differences were compared using a one-sided K-S test to compare differences in allelic RNA stability between groups. All scripts implementing analysis of allele-specific termination can be found at: https://github. com/Danko-Lab/F1_8Organs/tree/main/termination (F) The browsershot shows an example of ChRO-seq data that has an imprinted domain (top row).
Acknowledgements We thank  Maria Garcia-Garcia ,  Abdullah Ozer ,  John Lis ,  Hojoong Kwak ,  Gilad Barshad ,  Alexandra Chivu , and all members of the  Danko lab  for valuable discussions and suggestions throughout the life of this project. We thank  Peter Borst  for help preparing and working with F1 hybrid mice and  Jen Grenier  and the  Cornell TREx facility  for preparing mRNA-seq libraries. We thank  C. Kaplan  ( U. Pittsburgh ) for rapid constructive comments based on our bioRxiv preprint. Work in this publication was supported by  R01-HG010346  and  R01-HG009309  ( NHGRI ) to CGD. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  US National Institutes of Health. Some of the figures in this manuscript were created using BioRender.
Data analysis: Read mapping, transcription start site, and transcription unit discovery: Processing and mapping ChRO-seq reads: Paired-end reads with single nucleotide precision were processed and aligned to the reference genome (mm10) with the proseq2. 0 (https://github. com/Danko-Lab/proseq2. 0). Libraries in which we tailored the run-on to extend the length of reads were pre-processed, demultiplexed, and aligned to the reference genome (mm10) with the proseqHT_multiple_adapters_sequencial. bsh. AlleleDB  (Chen et al. , 2016; Rozowsky et al. , 2011)  align the R1 reads to the individual B6 and Cast genomes. In brief, the adaptor sequences were trimmed with the cutadapt, then PCR duplicates were removed using unique molecular identifiers (UMIs) in the sequencing adapters with prinseq-lite.
pl  (Schmieder and Edwards, 2011). The processed reads were then aligned with BWA (mm10) in analyses not using individual genome sequences  (Li and Durbin, 2009) , or with bowtie  (Langmead et al. , 2009)  as input for AlleleDB. When bowtie was used, we selected either the R1 or R2 files for alignment for analyses requiring either the 5' or 3' end of the RNA insert. All scripts for mapping can be found publicly at: https://github. com/Danko-Lab/F1_8Organs/blob/main/00_F1_Tissues_proseq_pipeline. bash https://github. com/Danko-Lab/utils/blob/master/proseq_HT/proseqHT_multiple_adapters_sequencial. bsh.
Two major attentional mechanisms that guide selective attention are stimulus-driven (bottom-up, saliency-related) exogeneous shift of attention and voluntary goal-directed (top-down) attention regulation  32. Thus, during an active task performance number, and duration of fixations on salient locations can indicate distraction  39, 40  , and potentially it can be used to indicate attentional lapses of children with ADHD  11. In this study, we collected eye movement data while the participants performed EPELI VR task (see https:// tinyu rl. com/ bdh74 72e). EPELI includes 13 task scenarios where the participants perform everyday chores in a virtual environment. Each task scenario has a general topic (e. g. , morning routines or coming back from school), and it consists of four to six subtasks (e.
g. , put backpack to our room, wash your hands, eat lunch). At the beginning of each task scenario, an animated dragon character gives instructions regarding all subtasks to be done. Task scenario finishes if all the subtasks are done, or if the time limit of 90 s is reached. The total duration on the game is 25-35 min and it includes 70 subtasks. In this study, we collected eye movement data of the participants playing EPELI with the aim of further improving the detection of ADHD and clarifying the specific mechanisms of attention deficits. In a previous study  12  , we reported performance for five task measures reflecting key aspects in ADHD symptoms. Compared to controls, ADHD children showed a higher percentage of irrelevant actions out of all actions suggesting poor attentional and executive control, longer navigation paths indicating problems with planning, tendency to perform excessive number of actions possibly associated with impulsive behavior, lower number of correctly performed tasks reflecting difficulty in executing the tasks from memory, and higher amount of controller movement that was used to index motor activity level.
We expected that these finding would be replicated, and that rich data provided by fixations and saccades during this task would further improve the prediction of group status. More specifically, our preregistered hypothesis (https:// tinyu rl. com/ yck2y 7u2) based on findings of studies with conventional experimental tasks was that salient objects in the environment would attract the gaze of ADHD children more effectively than in typically developing children and that focusing attention to relevant stimuli would be less efficient in ADHD children. Finally, we expected that ADHD group would demonstrate higher number of switches between ambient and focal processing due to fluctuating attention.
EPELI results. Task performance measures: We first examined group differences in five measures reported by Seesjärvi and colleagues 12 (see Fig. 1 ). A group difference was observed in all EPELI performance measures, with the ADHD group having lower Total Score (Z = 2. 31, p = 0. 021), Task Efficacy (Z = 3. 1, p = 0. 0048), and Navigation Efficacy (Z = 2. 73, p = 0. 0085), and higher level of Controller Motion (Z = 2. 7, p = 0. 0085) and Total Actions (Z = 3. 2, p = 0. 0048) than the control group. To examine how well we could identify the group membership of individual participants, we trained a supported vector machine  42  (SVM) classifier using data of the five EPELI performance measures reported above (SVM 1). This analysis resulted in 0. 77 Area Under the Receiver Operator Characteristic Curve (AUC score).
2 ). Fixation Duration was longer in the ADHD group as compared with the control group, but the effect was moderated by the Task Scenario (see Table  2  and Fig. 2 ). Additional models which test eye movement difference between the groups adjusted by gender provided in Supplementary Materials (see Supplementary Results: Statistical Analysis Adjusted for Gender). Correlation between the eye movement features and total score on ADHD-RS symptoms questionnaire can be found in the Supplementary Materials (Supplementary Results: Correlation between Eye Movements and ADHD-RS score). To test our main research question related to the predictive ability of eye movement features in EPELI, we trained an SVM classifier (SVM 2) on Fixation Duration, Saccade Duration, and Saccade Amplitude data aggregated per task scenario.
04, p = 0. 12; invalid data effect: χ 2 (1) = 6. 58, p = 0. 03) nor on Saccade Amplitude (χ 2 (1) = 1. 41, p = 0. 23, invalid data effect was not significant either, χ 2 (1) = 1. 40, p = 0. 36). The classification accuracy obtained with SVM 3 based on Shoot the Target eye movement data was 0. 78 AUC, which was worse than SVM 2 (t(58) = 9. 93, p < 0. 0001) performance utilizing EPELI eye movement data. Discussion. Cognitive deficits and behavioral problems of children with ADHD are heterogeneous and idiosyncratic. Therefore, conventional experimental methods with restricted designs, constraining the heterogeneity and tapping only narrow symptom domains at a time, may not be optimally suited to support diagnostics  2, 3. The present study shows that recent advances in VR technology can be used to detect attentional and executive deficits in a complex ecologically valid setting, and with high precision.
This opens new means to approach aberrant goal-directed behavior in future clinical studies and could be useful in studies with other clinical groups too (e. g. , autism, schizophrenia, mood disorders, neurodegenerative disorders). Overall, we did observe switches between ambient and focal processing modes during task performance, and this was also affected by familiarity of the environment. However, even though extended performance variability was reported in a previous study with EPELI task  6  and was also observed in conventional CPT task in the present dataset, we did not observe group differences eye movement fluctuations during the VR task. Future studies could look more closely into intra-individual variability metrics during performance of naturalistic VR tasks. However, it is possible that such effects are more prominent in conditions with stimulus poverty  23.
Methods Participants. The participants were accepted to the control group if they (1) did not have any mental, behavioral, and neurodevelopmental disorders (F00-F99 in ICD-10), nor any diseases of the nervous system (diagnosis G00-G99), (2) they were not entitled to special support at school for other reasons. These criteria were controlled for via a parent questionnaire. Additional criteria for the ADHD group were: (1) ADHD diagnosis (F90) made by a licensed medical doctor and verified via the National Medical Database, (2) no diseases of the nervous system (diagnosis G00-G99 in ICD-10), nor any other mental, behavioral, or neurodevelopmental disorders (F00-F99) except ADHD, and two possible comorbid diagnoses, F93. 89 (Emotional disorder with onset specific to childhood) and F93. 9 (Unspecified childhood emotional disorder).
Processing of eye movement data. Five-point eye-tracking calibration was performed before each VR task with Tobii inbuild software. Among the participants with eye-tracking data, two were excluded from the EPELI eye movement analysis based on high rate of invalid samples (80 and 30 precents). There were no differences in the percentage of the lost samples between the groups after the exclusion, and overall percentage of lost data was low (75 percentile -5%, maximum -16%). In the Shoot the Target data, the overall proportion of invalid gaze sample was rather small in both groups (75 percentile -1%, the maximum data loss -10%). However, the ADHD group had a higher percent of lost data points (Z = 3, p = 0. 002), so in the result section, we report the outcome of mixed model including percentage of invalid gaze samples as a random effect.
Saccades and fixations were detected from raw gaze data with a modification of Engbert and Kliegl's algorithm  57  for detecting eye movement events with unconstrained head movements  58. Head compensation was conducted by mapping raw gaze positions to world space coordinates. The eye movement features included were fixation duration, saccade duration, and saccade amplitude. Peak velocity was excluded from the analysis due to low sampling rate of the eye-tracker. Smooth pursuit detection was not included due to the lack of objects moving with suitable speed for smooth pursuit in the game. Saccades with duration below 22 ms (the threshold was selected as 1/sampling rate * 2), and with amplitude below 0. 75 degree (following the threshold suggested by Larsson and colleagues  58  ) were excluded from the analysis.
Acknowledgements This work was supported by the  Academy of Finland  (grants # 325981  and # 328954  to JS),  Finnish Cultural Foundation  (grant # 00210721  to LM),  Psychiatry Research Foundation  (grant # 20210019  LM),  Instrumentarium Science Foundation  (grant # 200005  to ES), and  Aalto Brain Centre. In addition, we want to thank all participants and their families, research assistants taking part in data collection, and  Aalto Science-IT  for providing computational resources, and technical support.
Genes involved in mucocyst exocytosis share similar patterns of expression, as shown by the transcriptional profiles of Tetrahymena Nd6, Nd9, NdP1, NdP2, and Fer2 genes in different life stages  (Fig 1D, left) , while genes involved in different pathways have nonmatching profiles  (Fig 1D, right). We, therefore, employed a bioinformatic tool specifically developed for Tetrahymena, called the Coregulation Data Harvester (CDH;  Tsypin & Turkewitz, 2017) , to screen for other proteins with comparable patterns in the Tetrahymena databases (TGD, http://ciliate. org; FGD, http://tfgd. ihb. ac. cn). Since we were interested in genes with a conserved function in exocytosis in Alveolata, with a particular focus on rhoptry exocytosis in Toxoplasma and Plasmodium, we refined our analysis and set up the CDH search to look for Tetrahymena genes conserved specifically in T.
gondii and P. falciparum  (Fig 1E). We performed the CDH analysis using Tetrahymena Nd6, NdP1, NdP2 and Fer2 as separate queries, but excluded TtNd9 due to its very low expression level. The CDH program identified those Tetrahymena genes coexpressed with each selected query, and with homologs in T. gondii and P. falciparum, by BLAST and reciprocal BLAST. We then prioritized a list of candidates shared by at least three of the four queries  (Fig 1E and Dataset EV1). Among the 37 Tetrahymena candidates identified, three (TTHERM_00442310, TTHERM_00637180, and TTHERM_00348670) encode proteins containing similar features including an epidermal growth factor receptor domain and multiple C-terminal transmembrane domains. These domains are shared by the putative homologs found in T. gondii and P. falciparum  (Fig 1F).
I AV dimensions (AV maj and AV min ). J AV eccentricity (Ecc) calculated using the AV dimensions (AV maj and AV min ) in panel (I). K AV orientation parameter (Ψ') that measures the AV anchoring angle. Data information: Panels (H), (J), and (K) show a combination of boxplot and swarmplot for each dataset. For the boxplot, the lower and upper boundaries of the box represent the first and third quartiles (Q1 and Q3), whiskers extend to 1. 5 times the interquartile range (Q3-Q1) below and above Q1 and Q3, and points outside (diamonds) are regarded as outliers. The horizontal divider within the box represents the median with the value noted next to it. For the swarmplot, each data point represents a measurement from a tomogram. Panel (I) shows a jointplot, which is a combination of a bivariate scatterplot and two marginal univariate kernel density estimate plots (a.
Homology searching and phylogenetic tree construction for ferlin genes Toxoplasma gondii ferlin 2 protein sequence (TgFer2; TGME49_260470) was used as a query against translated open reading frame (ORF) coding sequences from the genomes of selected Ciliata and Apicomplexa species using the BLAST algorithm. Positive BLAST hits against TgFer2 query were those with an E value < 0. 001 and score > 50, for which reciprocal BLAST against the genome containing the query sequence retrieved either the same sequence or an isoform of the sequence with a similar E value or lower. Once homologs of TgFer2 were identified by BLAST, the phylogenetic relationships of ferlins within ciliates and apicomplexans were determined by maximum-likelihood estimation. Homologs were aligned by MUSCLE and tree construction was performed by MEGA11 software  (Tamura et al, 2021).