dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this article, we have presented an analysis of colors of dark asteroids from the SDSS dataset with a focus on "C-type". We found that: -A cluster analysis based on gri slope, z-i and albedo enables to identify a cluster that can be assigned to "C-type" with boundaries in the z-i vs gri slope space close to the ones defined by DeMeo and  Carry (2013). The analysis does not identify a B-type cluster, but rather enlightens the presence of a continuum of objects with varying visible slopes. -CM chondrites are the closest match to the C-cluster identified. Other types of carbonaceous chondrites studied here are not (CO-CV-CR and Tagish Lake). Carbonaceous chondrites of CO, CR and CV groups have colors more similar to X-, K-and L-type as defined in DeMeo and  Carry (2013). -There appears to be a size dependency of the colors of C-cluster members, which merits further investigation and confirmation by other datasets.
The smaller objects (D<20 km) appear bluer than larger objects. The preferred explanation is that smaller C-cluster asteroids have surfaces covered by rocks, or large grained material (>1 mm) rather than a fine-grained regolith. This suggest that a size-sorting mechanism may act under microgravity environment. Possible mechanisms include escape of finer material during impact of the "Brazil-nut" effect. -CM chondrites powders are slightly redder than large C-cluster asteroids that are expected to be covered by regolith. This difference may be attributed to high-energy irradiation based on available laboratory data. which explains the absence of high-inclination objects in the plots as well as objects with semi-major axis above 3. 6. Figure  4 : The C-cluster compared to colors of carbonaceous chondrites (meteorite spectra from the RELAB database, except for Tagish Lake measured at IPAG).
With increased usage of long-read sequencing technologies to perform transcriptome analyses, there becomes a greater need to evaluate different methodologies including library preparation, sequencing platform, and computational analysis tools. Here, we report the study design of a community effort called the Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium, whose goals are characterizing the strengths and remaining challenges in using long-read approaches to identify and quantify the transcriptomes of both model and non-model organisms. The LRGASP organizers have generated cDNA and direct RNA datasets in human, mouse, and manatee samples using different protocols followed by sequencing on Illumina, Pacific Biosciences, and Oxford Nanopore Technologies platforms. Participants will use the provided data to submit predictions for three challenges: transcript isoform detection with a high-quality genome, transcript isoform quantification, and de novo transcript isoform identification.
Evaluators from different institutions will determine which pipelines have the highest accuracy for a variety of metrics using benchmarks that include spike-in synthetic transcripts, simulated data, and a set of undisclosed, manually curated transcripts by GENCODE. We also describe plans for experimental validation of predictions that are platformspecific and computational tool-specific. We believe that a community effort to evaluate longread RNA-seq methods will help move the field toward a better consensus on the best approaches to use for transcriptome analyses.
Box 1: Overview of the LRGASP Challenges The LRGASP Challenges will use data produced by the LRGASP Consortium Organizers (Fig   1 ). The samples for Challenges 1 and 2 consist of human and mouse ENCODE biosamples with extensive chromatin-level functional data generated separately by the ENCODE Consortium. These include the human WTC-11 iPSC cell line and a mouse 129/Casteneus ES cell line for Challenge 1 and a mix of H1 and Definitive Endoderm derived from H1 (H1-DE) for Challenge 2. In addition, individual H1 and H1-DE samples are being sequenced on all platforms; however, those reads will not be released until after the end of the challenge. All samples were grown as biological triplicates with the RNA extracted at one site, spiked with 5'-capped Spike-In RNA Variants (Lexogen SIRV-Set 4), and distributed to all production groups.
After sequencing, reads for human and mouse samples were deposited at the ENCODE Data Coordination Center (DCC) for community access, including but not limited to the challenges. A single replicate of manatee whole blood transcriptome was generated for Challenge 3. For each sample, we performed different cDNA preparation methods, including an early-access ONT cDNA kit (PCS110), ENCODE PacBio cDNA, R2C2  13  for increased sequence accuracy of ONT data, and CapTrap to enrich for 5'-capped RNAs. CapTrap is derived from the CAGE technique  14  and was adapated for lrRNA-seq (manuscript in preparation). We also performed direct RNA sequencing (dRNA) with ONT. Participants may provide multiple submissions for each challenge (detailed in Challenge submissions and timeline) and in any or all challenges.
Sample # of Reps PacBio Depending on the challenge, they will submit either a GTF or quantification file, additional metadata, and a link to a repository (e. g. , Github) where a working copy of the exact analysis pipeline used to generate their results can be downloaded. We expect to re-run analysis pipelines for well-performing submissions to help ensure reproducibility. The evaluation of the challenge will comprise both bioinformatics and experimental approaches. SQANTI3 (https://github. com/ConesaLab/SQANTI3) will be used to obtain transcript features and performance metrics that will be computed on the basis of SIRV-Set 4 spike-ins, simulated data, and a set of undisclosed, manually curated transcript models defined by GENCODE  15. Human models will further be compared to histone modification ChIP-seq, open chromatin, CAGE, and poly(A)-seq results.
Long-read data processing Basecalling of ONT data from human, mouse and manatee was performed with Guppy 4. 2. 2 and hac 9. 4. 1 config file, with default parameters, except: --qscore_filtering --min_qscore 7 (these non-default parameters were used in all ONT cDNA runs except for R2C2 datasets). Direct RNA basecalling was also performed with Guppy 4. Table 1 ).
Simulated data Simulating RNA reads simply from the reference transcriptome would only allow the assessment reconstruction of known transcript models. Thus, we extended both human and mouse annotations with artificial novel transcripts. To obtain those, we mapped reference transcripts of an undisclosed mammalian organism to the human and mouse genomes and converted the alignments into transcript models using SQANTI  20. We then arbitrarily selected isoforms of known genes that have only canonical splice sites (GT-AG, GC-AG and AT-AC) and merged them into human and mouse GENCODE Basic annotations. To generate realistic isoform expression profiles we selected undisclosed human and mouse long read datasets and quantified them simply by mapping to the reference transcripts with minimap2 v2. 17 (Li, 2018).
Artificial novel isoforms were assigned arbitrary expression values. Generated expression profile was further used for simulating short and long reads. To simulate reads produced by different sequencing platforms we used existing simulation methods. Illumina 2x150bp read pairs were generated with the RSEM simulator 21 using an error model obtained from real RNA-Seq data  22  ( accession number ERR1474891). ONT reads were simulated with NanoSim 23 using pre-trained cDNA and dRNA models available in the package with average error rate of 15. 9% (4. 8% substitutions, 6. 0% deletions, 5. 1% insertions) and 11. 2% (2. 8% substitutions, 5. 9% deletions, 2. 5% insertions) respectively. PacBio CCS reads were obtained with IsoSeqSim (https://github. com/yunhaowang/IsoSeqSim), which truncates input reference transcript sequences and uniformly inserts errors according to given probabilities.
We used Sequel II truncation probabilities provided along with the package. Error rate was estimated using real PacBio cDNA CCS reads obtained in this work as 1. 6% (0. 4% substitutions, 0. 6% deletions, 0. 6% insertions). Additionally, polyA tails were attached to the 3' end of reference transcript sequences prior to running the simulation. We simulated two datasets containing reads from all 3 platforms listed above but with slightly.
GENCODE benchmarks and computational evaluation Full manual annotation will be undertaken on 50 selected loci on both the human and mouse reference genomes. Transcript models will only be annotated during this exercise based on their support from long transcriptomic datasets generated by the consortium specifically for LRGASP. That is, no transcript annotation will be based on transcriptomic data from externally produced datasets, although annotators will use any publicly available orthogonal data to aid interpretation of aligned consortium data. For example, Fantom 5 CAGE datasets will be used to help identify transcription start sites and transcript 5' ends and RNA-seq-supported introns derived from high throughput reanalysis pipelines such as Recount will be used to support putative introns identified in the alignments of long transcriptomic data.
Manual annotation will be performed according to the guidelines of the HAVANA (Human And Vertebrate Analysis aNd Annotation) group  15, 27. Transcriptomic data will be aligned to the human and mouse reference genome using appropriate methods. We will test the benefits of aligning the transcriptomic data using multiple methods to reduce the impact of alignment errors and artefacts. Annotators will also take advantage of local alignment tools integrated into annotation software to give further alternative views of alignments and improve annotation accuracy. Transcript models will be manually extrapolated from the alignments by annotators using the otter annotation interface 28. Alignments will be navigated using the Blixem alignment viewer  29, 30  and where required visual inspection of the dot-plot output from the Dotter tool 31 will be used to resolve any alignment with the genomic sequence that was unclear or absent from Blixem.
Challenge 1 Evaluation: Transcript isoform detection Four sets of transcripts will be used for evaluation of transcript calls made on human and mouse lrRNA-seq data 1. Lexogen SIRV-Set 4 (SIRV-Set 3 plus 15 new long SIRVs with sizes ranging from 4 to 12 kb) 2. Comprehensive GENCODE annotation: human v39, mouse vM28. GENCODE human v28 and vM27 are available at the time of the LRGASP data release and new versions of GENCODE will be released after the close of LRGASP submissions. 3. A set of transcripts from a subset of undisclosed genes which will be manually curated by GENCODE. These transcripts will thus be considered high-quality models derived from LRGASP data 4. Simulated data for both Nanopore (Nanosim) and PacBio (Iso-SeqSim) reads The rationale for including these different types of transcript data is that each set creates a different evaluation opportunity, but also has its particular limitations.
For example, SIRVs and simulated data provide a clear ground truth that allows the calculation of standard performance metrics such as sensitivity, precision or false discovery rate. Evaluation of SIRVs can identify potential limitations of both library preparation as well as sequencing, but the SIRVs themselves represent a dataset of limited complexity. Higher complexity can be generated when simulating long reads based on actual sample data. However, read simulation algorithms only capture some potential biases of the sequencing technologies (e. g. , error profiles) and not of the library preparation protocols. In any case, both types of data approximate, but do not fully recapitulate real-world datasets. Evaluation against the GENCODE annotation 15 represents this real dataset scenario, although in this case the ground truth is not entirely known.
This limitation will be partially mitigated by the identification of a subset of GENCODE transcript models that will be revised and deemed as high-confidence by GENCODE curators, and by follow-up experimental validation for a small set of transcripts using semi-quantitative RT-PCR and quantitative PCR (qPCR) approaches. In this way, although an exhaustive validation of the real data is not possible, estimates of the methods' performances can be inferred. By putting together evaluation results obtained with all these different benchmarking datasets, insights will be gained on the performance of the library preparation, sequencing and analysis approaches both in absolute and in relative terms. The evaluation of the transcript models will be guided by the use of SQANTI categories  20   Given these definitions, evaluation metrics are specified for each type of data.
Simulated Data The simulated data contains both transcript models based on the current GENCODE annotation and a number of simulated novel transcripts that will result in true NIC and NNC annotations. Transcript models generated from simulated data will be analysed by SQANTI3 providing a GTF file that includes all simulated transcripts (GENCODE and novel) and excludes all transcripts for which reads were not simulated. The evaluation metrics for simulated data are defined as follows:.
Challenge 3 Evaluation: De novo transcript isoform detection without a high-quality genome Challenge 3 will evaluate the applicability of lrRNA-seq for de novo delineation of transcriptomes in non-model organisms. The evaluation will assess the capacity of technologies and analysis pipelines for both defining accurate transcript models and for correctly identifying the complexity of expressed transcripts at genomic loci, when genome information is limited. We will evaluate two different scenarios: a) availability of a genome sequence but no gene annotation is available, and b) no genome assembly is available at all. The challenge includes three types of datasets. The mouse ES transcriptome data (Table  1 ) will be used to request the reconstruction of mouse transcripts without making use of the available genome or transcriptome resources for this species.
Models will be compared to the true set of annotations with the same set of parameters as in Challenge 1. While this dataset allows for a quantitative evaluation of transcript predictions in Challenge 3, it might deliver unrealistic results if analysis pipelines were somehow biased by information derived from prior knowledge of the mouse genome. To avoid this problem, a second dataset is used that corresponds to the whole blood transcriptome of the Floridian manatee (Trichechus matatus). An Illumina draft genome of this organism exists (https://www. ncbi. nlm. nih. gov/assembly/GCF_000243295. 1/) and the LRGASP consortium has generated a long-read genome assembly to support transcript predictions for this species. Additionally, Illumina data has been generated for this challenge and an existing set of 454 transcriptome data will be used.
Again, we will evaluate pipelines that obtain transcript models without genome annotation but with these draft genome sequences, and without genome assembly data at all. Since no curated gene models exist for the manatee, Challenge 1 metrics cannot be applied. Instead, the evaluation of this dataset will involve comparative assessment of the reconstructed transcriptomes and experimental validation. For comparative assessment the following parameters will be calculated. We expect that good-performing pipelines will obtain longer transcripts, well supported by Illumina data, with high mapping rate to the draft genomes, most of them coding, and with higher BUSCO completeness and Blast2GO annotation potential. Finally, the manatee long reads data also contain spiked-in SIRVs, which will be used to compute performance metrics for Challenge 3 analysis settings, using the same type of metrics as described for Challenge 1.
Experimental validation of transcript models and expression estimates Independent experimental validation will be performed to assess the accuracy of novel features and transcript isoforms characterized from the lrRNA-seq data from all challenges. In the evaluation of full-length transcripts, several local and long-range elements must be considered. Local elements include the 5' end of the transcript, splice site, junctions, novel exons, retained introns, and polyA sites. Long-range elements include chained series of junctions. We will employ a suite of several assays in order to validate both the local and long-range elements.
Challenge 1 Evaluation: Transcript isoform detection The goal of this challenge is to assess the comprehensive and reliable detection of all transcripts in biological samples. Similar to past studies that have employed lrRNA-seq approaches towards characterizing the transcriptome, we expect that participants for this challenge will produce a large number of novel isoforms. Therefore, the approaches to assess the accuracy of transcript isoforms that were previously described (e. g. , SIRV standards, GENCODE manual annotation) will be complemented with experimental validation. We will employ several high-throughput sequencing-based assays to validate local elements, such as novel 5' ends, splice junctions, and polyA sites, on a "global" scale. Note that these experimental assays have or will be carried out using the same aliquot of total RNA as was used to generate the LRGASP datasets, minimizing differences in detected features due to biological or inter-laboratory variability.
To validate novel 5' ends, we will use a recently generated a deep coverage CAGE data on the WTC-11 line. To validate novel splice junctions, we will also use Illumina RNA-seq to validate novel junctions and, wherever possible, exons or series of connected exons. To validate novel polyadenylation sites, we will collect polyA-seq data using the Quant-Seq method from Lexogen, which can map polyA sites de novo. Additionally, in select cases, novel 5' ends will be further corroborated through chromatin-based functional information derived from ENCODE data, such as the presence of PolII or histone marks that are indicative of active promoters. Longer-range features within a transcript, such as chains of junctions, are difficult and sometimes impossible to detect through short-read sequencing approaches or traditional qPCR; therefore, we will employ targeted amplicon sequencing followed by ONT, PacBio, and Sanger sequencing.
We plan to select 96 targets from human WTC-11 cells and 96 targets from the mouse 129/Casteneus cells. Each target will comprise a sequence region 300 to 1500 bp long. Two replicates each from the WTC-11 and 129/Casteneus sample will be apportioned for a reversetranscriptase reaction followed by target amplification using isoform-specific primers. We will conduct the assay in plate format to allow for high-throughput processing. All products following RT-PCR will be pooled and subjected to long-read sequencing for validation. A subset of these samples will be selected for Sanger sequencing. Table  6  shows the breakdown of targets we will select. Positive controls will be selected as subsegments of isoforms which are found in GENCODE human v39 and mouse vM28, all long-read datasets across the ONT and PacBio platforms, and a majority (>50%) of the computational pipelines.
with the following configurations: --qscore_filtering yes --min_qscore 7 --reverse_sequence yes --u_substitution yes PacBio full-length non-chimeric (FLNC) reads were generated with CCS 4. 2. 0 (parameters: --noPolish --minLength=10 --minPasses=3 --min-rq=0. 9 --min-snr=2. 5), Lima 1. 11. 0 (parameters: FASTA with the appropriate adapters --isoseq --min-score 0 --min-end-score 0 --min-signalincrease 10 --min-score-lead 0), and Refine 3. 3. 0 (parameters: --min-polya-length 20 --requirepolya). Consensus R2C2 reads were generated with C3POa v1. 0. 0 (https://github. com/rvolden/C3POa/tree/gonk) with default options Sequence data are provided in FASTQ format. For PacBio data, subreads are provided in unaligned BAM format and for R2C2 data, subreads are provided in FASTQ (Supplementary.
different properties. Human datasets were simulated with 100 million Illumina read pairs, 30 million ONT cDNA and 10 million PacBio reads. Mouse datasets also contained 100 million Illumina read pairs, but equal amounts of PacBio CCS and ONT dRNA reads were generated (20 million sequences each). To allow users to simulate their own data, the methods described above are implemented as simple command-line scripts which are available at https://github. com/LRGASP/lrgaspsimulation/. CAGE data of WTC-11 samples for validation of transcript 5' endsCAGE data from WTC-11 samples are being produced for validation of transcript 5' ends; therefore, will not be released until the close of the challenge submissions. CAGE data will be obtained from two RNA biological replicates of WTC-11, from the same exact RNA used for long-read sequencing.
implemented in the SQANTI3 software (https://github. com/ConesaLab/SQANTI3), and will incorporate additional definitions and performance metrics to provide a comprehensive framework for transcript model assessment (Table2). The evaluation considers the accuracy of the transcript models both at splice junctions and at 3'/ 5' transcript ends. It will take into account external sources of evidence such as CAGE data, polyA annotation and support by Illumina reads (Fig 2b). A number of novel transcripts detected by all or most pipelines, as well as pipeline-, platform-, or library-preparation specific transcripts will be selected for experimental validation and manual review by the GENCODE project. The evaluation script is provided to participants (Data and code availability). Transcript Classifications and Definitions used by the LRGASP computational evaluation Classification Description Full Splice Match (FSM) Transcripts matching a reference transcript at all splice junctions Incomplete Splice Match (ISM) Transcripts matching consecutive, but not all, splice junctions of the reference transcripts Novel in Catalog (NIC) Transcripts containing new combinations of 1) already annotated splice junctions, 2) novel splice junctions formed from already annotated donors and acceptors, or 3) unannotated intron retention Novel Not in Catalog (NNC) Transcripts using novel donors and/or acceptors Reference Match (RM) FSM transcript with 5´ and 3´ends within 50 nts of the transcription start site (TSS)/transcription termination site (TTS) annotation _3´_polyA_supported Transcript with polyA signal sequence support or short-read 3' end sequencing (e.
4. 2 42 with the following configurations: --qscore_filtering yes --min_qscore 7 --reverse_sequence yes --u_substitution yes PacBio full-length non-chimeric (FLNC) reads were generated with CCS 4. 2. 0 (parameters: --noPolish --minLength=10 --minPasses=3 --min-rq=0. 9 --min-snr=2. 5), Lima 1. 11. 0 (parameters: FASTA with the appropriate adapters --isoseq --min-score 0 --min-end-score 0 --min-signalincrease 10 --min-score-lead 0), and Refine 3. 3. 0 (parameters: --min-polya-length 20 --requirepolya). Consensus R2C2 reads were generated with C3POa v1. 0. 0 (https://github. com/rvolden/C3POa/tree/gonk) with default options Sequence data are provided in FASTQ format. For PacBio data, subreads are provided in unaligned BAM format and for R2C2 data, subreads are provided in FASTQ (Supplementary.
Velocity Curves Velocity curves of the ObPOX activity were obtained at 20 °C by using constant amounts of H 2 O 2 (0. 26 mM) and the enzyme (49 nM) in Tris-base buffer (0. 05 M, pH 7. 5) containing different concentrations of i) guaiacol (5. 0-85 mM), and ii) GASA (4. 0-100 µM). The velocity curve of ObPOX for H 2 O 2 (0. 03-2. 12 mM) was obtained by using a constant concentration of guaiacol (5. 0 mM) in the same buffer and temperature. All the presented data are the average of, at least, three repeated experiments. Enzymatic Kinetics constants (K m , k cat , k cat /K m ) for the substrates were calculated from the corresponding Lineweaver-Burk plots of the data and the results were double checked by Eadie-Hofstee method.