dataesr/scientific-paragraphs-categorization huggingface dataset:
We were able to show that creating rich gRNA annotations can help investigate gRNA variability and biases observed in experimental data generated from newer CRISPR technologies. To do so, we obtained public pooled screening data from two published studies, a tiling base editor screen of BRCA1, and a tiling CasRx screen of CD46 and CD55, and show how some of the gRNA features derived from crisprDesign can explain some of the variability in gRNA activity observed in both screens. We also showed that our default gRNA ranking criteria implemented in crisprDesign yield optimal gRNAs by reanalyzing five genome-wide fitness screening datasets. The modular architecture of the crisprVerse enables nucleases, base editors, scoring methods and annotations to be combined depending on the needs of the user.
Reference genomes, gene models, and genome indexes The FASTA file for the human reference genome (GRCh38. p13 assembly) was obtained from UCSC to build Bowtie and BWA indexes via the Rbowtie (v1. 37)  [Hahne et al. , 2012]  and Rbwa (v1. 1) R packages, respectively. The packages use Bowtie v1. 3 and BWA Release 0. 7. 17, respectively. The gene model used throughout the manuscript was obtained from Ensembl (release 104) using the R package GenomicFeatures (v1. 49. 6). Common SNPs were obtained from NCBI dbSNP build 151 (https://ftp. ncbi. nlm. nih. gov/snp/).
CAGE peak and DNAse I hypersensitivity data RIKEN/ENCODE CAGE peaks were obtained from AnnotationHub (v3. 5) using accession number AH5084  [Djebali et al. , 2012]. Genomic coordinates were lifted over from hg19 to hg38 using the R package rtracklayer (v1. 57). DNAse I hypersensitive sites were obtained from AnnotationHub using accession number AH30743. The narrow DNase peaks were obtained using MACS2 on consolidated epigenomes from the Roadmap Epigenomics Project (E116-DNase. macs2. narrowPeak. gz) [Kundaje et al. , 2015]. Genomic coordinates were lifted over from hg19 to hg38 using the R package rtracklayer.
The chromatin data in hg38 coordinates are available on Zenodo (DOI: 10. 5281/zenodo. 6716721). The function addCompositeScores from crisprDesign creates an aggregate score from a specified list of ontarget scoring methods. It takes the average of the specified scores after performing a rank transformation. More specifically, consider s ij to be the score value for gRNA i and method j. The composite score S i for gRNA i is S i = N j=1 rank(s ij ) N (1) where N is the total number of user-specified on-target scoring methods, and rank(s ij ) is the ranked score within method j. Importantly, if the number of missing values varies across on-target scoring methods, we ensure that the scale of the rank-transformed values are comparable across methods by simply scaling the ranks so that highest ranked value is equal across all methods.
Evolutionary conservation scores The function addConservationScores in crisprDesign annotates gRNAs with evolutionary conservation scores. It requires bigWig files containing basewise conservation scores, which can be easily obtained from the UCSC genome browser database  [Karolchik et al. , 2003]  at the following link: https://hgdownload. soe. ucsc. edu/ downloads. html. The gRNA score is calculated as the average conservation score of a region centered around the predicted cut site of the gRNA. By default, the width of the region is 18 nucleotides, but can be changed by users. For our analysis of human protein-coding genes, we used the phyloP score from an alignment of 29 genome sequences to the human genome available at https://hgdownload. soe. ucsc. edu/goldenPath/hg38/ phyloP30way/. Positive phyloP scores indicate conserved regions, while negative scores indicate evolution faster than expected under neutral drift.
Creation of major and minor allele human genomes We built major and minor allele genomes for the hg38 build using SNPs from the dbSNP151 RefSNP database. The "common" category is based on germline origin and a minor allele frequency (MAF) of >= 0. 01 in at least one major population, with at least two unrelated individuals having the minor allele. See the dbSNP website https://www. ncbi. nlm. nih. gov/variation/docs/human_variation_vcf/ for more information. We excluded indels, and only considered SNPs that have MAF greater than 1% in the 1000 Genomes Project population. We then injected major alleles and minor alleles into the reference genome hg38 sequence to create "major allele" and "minor allele" genomes, respectively. Both resulting genomes are provided as standard FASTA files. We generated Bowtie and BWA indexes for the two genomes.
All results files are available on Zenodo (DOI: 10. 5281/zenodo. 6862556). The two allele genomes are also available from Bioconductor via their respective packages: • BSgenome. Hsapiens. UCSC. hg38. dbSNP151. major [Fortin, 2021a]  • BSgenome. Hsapiens. UCSC. hg38. dbSNP151. minor [Fortin, 2021b]  Base editing pooled screen data analysis Fitness screen data in the MelJuSo cell line using a gRNA library tiling BRCA1 were obtained from the supplementary material of Hanna et al. [2021]. We normalized the raw counts by scaling by the total number of reads, and log 2 -transformed the data. We filtered out low-abundance gRNAs that were further than 3 standard deviations below the mean in the plasmid (pDNA) sample. From the later timepoint samples, we subtracted from the pDNA sample log counts to obtain LFCs, and averaged the LFCs across replicates.
CasRx pooled screen data analysis CasRx FACS pooled screening data tiling CD55, CD46 and GFP were obtained from  Wessels et al. [2020] , including processed and normalized LFCs for each gRNA https://gitlab. com/sanjanalab/cas13. We redesigned all possible gRNAs targeting any of the isoforms of CD55 and CD46 using crisprDesign, and considered only gRNAs also present in the pooled screening data for downstream analyses. We annotated all gRNAs with gene information (Ensembl release 104) and obtained off-targets with up to 3 mismatches for all gRNAs using crisprBowtie. We obtained CasRx-RF on-target activity scores using crisprScore. The transcripts annotated as canonical by Ensembl (ENST00000367042 for CD46, and ENST00000367064 for CD55 ) were used to visualize LFCs. For each gRNA, we quantified the abundance of its target gene by summing transcript per million (TPM) counts in HEK-293 cells for all transcripts targeted by the gRNA.
Given the sparsity of the data, specifying a nucleotide-specific weight at each position was not possible. We adapted in crisprScore the CFD off-targeting scoring method to CasRx by using those probabilities as scoring weights. The corresponding scoring algorithm is named CFD-CasRx. To evaluate the performance of the CFD-CasRx score on an independent dataset, we calculated CFD-CasRx off-target scores on all SM and double-mismatch (DM) gRNAs included in the CD55 tiling screen. To predict LFCs of the DM gRNAs, we multiplied their respective PM gRNA LFCs with the CFD-CasRx on-target scores.
Comparison of off-target alignments across tools We compared computing times for designing SpCas9 gRNAs and performing a genome-wide off-target search for the following tools: CCTop, CHOPCHOP, multicrispr, FlashFry, and crisprDesign. The following tools were excluded from the comparison: CRISPick as it does not provide a standalone software; CRISPRseek as we were not able to complete the search within a reasonable time; Cas-Designer due to its requirement for specialized software that we were not able to install on our machines; E-CRISP as it was not possible to run their command line interface on customs DNA sequences or exons. To perform the comparison, we generated six random subsets of protein-coding exons located on chr1 with the following sizes:100, 200, 400, 800, 1600 and 3200 exons.
Processing of genome-wide screen datasets Achilles dataset: CRISPRko fitness screening gRNA-level LFCs from Project Achilles (22Q2 release) were downloaded from the DepMap portal https://depmap. org/portal/download/all/. Processed LFCs representing changes in gRNA abundances between the last time point of the fitness screen and the plasmid DNA were available for 957 human cell lines. Reference lists of essential and non-essential genes were downloaded from  Hart et al. [2014]. For each cell line, we first centered LFCs using the median value of the set of non-essential genes, and then scaled LFCs using the median value of the set of essential genes. This enables normalized LFCs to be comparable across cell lines. For each gRNA, we then summarized gRNA activity by averaging LFCs across cell lines.
Hart2015 dataset: Processed data from a genome-wide screen performed in HCT116 cells using the Toronto Knockout v1 (TKOv1) library  [Hart et al. , 2015]  were downloaded from http://tko. ccbr. utoronto. ca/. We Criteria used to compare feature availability across gRNA design tools The following gRNA design tools were used for comparison in Table  1 : multicrispr (v1. 7. 0), CRISPRseek (v1. 37. 2), CHOPCHOP (v3), CRISPOR (website v5. 01), CCTop (v1. 0. 0), Guides (v1. 0), Cas-Designer (v3. 0), FlashFry (v1. 15), E-CRISP (v5. 4) and CRISPick (no version, accessed on July 27 2022). The criteria listed below were used for assessing feature availability. Nuclease section: a check mark indicates support for the corresponding nuclease, and Limited indicates that only a subset of custom nucleases are available.
R packages in the crisprVerse ecosystem. found at https://github. com/crisprVerse/crisprDesignPaper. A list of extensive tutorials can be found at https://github. com/crisprVerse/Tutorials. The analyses included in this paper were produced using the following package versions: crisprDesign (v0. 99. 134), crisprScore (v1. 1. 14), crisprScoreData (v1. 1. 3), crisprBowtie (v1. 1. 1), crisprBase v(1. 1. 5), crisprVerse v(0. 99. 8), crisprBwa (v1. 1. 3), Rbwa (v1. 1. 0), crisprDesignData (0. 99. 17), crisprViz (0. 99. 18) We also offer a Docker container encapsulating the latest crisprVerse ecosystem on our DockerHub page. Documentation about the installation and usage of the container can be found here. b a Supplementary Figure  S5. Probability weights used for off-target scoring of CasRx gRNAs a Boxplots of the differences in log2 fold change (∆LFC) between single-mismatch (SM) gRNAs and their corresponding perfect-match (PM) gRNAs in the GFP tiling screen.
Colors represent variant classification: nonsense in red, missense in blue, silent in grey. The size of the dot is proportional to the on-target efficiency DeepHF score. On the right, ROC curves for classifying gRNA mutation type (nonsense or not) based on gRNA dropout from the BRCA1 BE4max dataset (see Methods). Different thresholds of the nonsense score were used to label a gRNA as nonsense or not. d Relationships between gRNA dropout from the BRCA1 BE4max dataset and several on-target activity scores. gRNAs that are not predicted to induce a nonsense mutation are colored in grey, and the size of the dots is proportional to the magnitude of the mutation score. The horizontal dotted lines at -0. 5 represent a cutoff to classify a gRNA as active or not. For each method, a score cutoff was determined to classify active versus non-active gRNAs (vertical dotted line).
Acknowledgements We thank  Benjamin Haley ,  Mike Costa ,  Amy Heidersbach ,  Kristel Dorighi ,  Scott Martin ,  Rena Yang ,  Allison Vuong ,  Oleg Mayba ,  Sandra Melo Carlos , and  Russell Xie  for sharing their expertise with us and guiding the development of our software ecosystem. We also thank  William Forrest ,  Maggie Crow ,  Hector Corrada Bravo ,  Michael Lawrence , and  Benjamin Haley  for providing invaluable feedback on the manuscript and software. We thank  Nitesh Turaga ,  Lori Shepherd ,  Marcel Ramos ,  Helena Crowell  and  Kayla Morrell  who kindly and thoroughly reviewed our R packages as part of the Bioconductor submission process. Finally, we want to thank the referees for their invaluable input and suggestions.
Data availability Chromatin accessibility data from  Horlbeck et al. [2016]  necessary for the CRISPRai on-target algorithm are available on Zenodo (DOI: 10. 5281/zenodo. 6716721). Fasta files, Bowtie indexes and BWA indexes for the major and minor alleles of hg38 using dbSNP151 are available on Zenodo (DOI: 10. 5281/zenodo. 6862556). We precomputed and fully annotated gRNAs for human and mouse protein-coding genes using crisprDesign for the following nucleases: SpCas9, enAsCas12a, and CasRx. Ensembl release 104 and Ensembl release 102 were used to define genes for human and mouse, respectively. Separate datasets were generated for the CRISPRko, CRISPRa, CRISPRi, and CRISPRkd modalities. All files are available on Zenodo (DOI: 10. 5281/zenodo. 7042164).
A line list for two isotopologues of aluminium monochloride, AlCl 35 and AlCl 37 are presented covering the wavelength range < 0. 2 𝜇m, 𝐽 up to 400 and applicable for temperatures up to 5000 K. The line lists are built using an empirical spectroscopic model consisting of potential energy curves, spin-orbit coupling curves, electronic angular momentum curves and Born-Oppenheimer Breakdown correction curves combined with ab initio dipole moments and covers the four lowest electronic states, 𝑋 1 Σ + , 𝐴 1 Π, 𝑎 3 Π and 𝑏 3 Σ +. Considerable problems with the assignments of some the laboratory rovibronic spectra are identified. Treatment of the states lying in the continuum is discussed. The YNAT line list is available from the ExoMol database at www. exomol. com.
1979 Ram et al. , 1982;; Saksena et al. 1998; Welz et al. 2006; Mahieu et al. 1989a,b; Daniel et al. 2021) , see also the summary by  Welz et al. (2006). Recent interest in AlCl has been sparked by the proposal that is an excellent candidate for laser cooling  (Daniel et al. 2021 ). An empirical AlCl line list was provided by  Yousefi & Bernath (2018)  and made available as part of the ExoMol database  (Wang et al. 2020 ). However, this line list only considers transitions within the ground 𝑋 1 Σ + state (𝑣 = 0 to 𝑣 = 11 and up to 𝐽 max = 200). There have been a number of theoretical studies on the electronically excited states of AlCl, see  Langhoff et al. (1988)  and  Xu et al. (2020)  for example, but none of them provide line lists. However, ab initio temperature-dependent direct photodissociation cross sections and rates for AlCl were recently reported by  Qin et al.
Ab initio calculations To provide a starting point for construction of a spectroscopic model for the four-electronic-state system 𝑋 1 Σ + , 𝐴 1 Π, 𝑎 3 Π and 𝑏 3 Σ + of AlCL, we computed ab initio all relevant PECs, SOCs, EAMCs, DMCs and TDMCs at the MRCI+Q/aug-cc-pVQZ level of theory using program MOLPRO  (Werner et al. 2012). The calculations used a (C 2𝑣 symmetry) active space of  (11, 4, 4, 1) , internally-contracted state-averaging using 5, 4, 4, and 2 singlet states of symmetry 1 A 1 , 1 B 1 , 1 B 2 , 1 A 2 , respectively and 1,1,1 triplet states of symmetry 3 A 1 , 3 B 1 , 3 B 2 , respectively. All curves were computed on a dense grid of non-uniformly distributed 162 points, between 𝑟 = 0. 7 and 9. 5 Å.
Therefore for 27 Al 37 Cl we simply use the infrared emission data of  Hedderich et al. (1993)  which we found to be reliable for both isotopologues. This allowed us to characterise the BOB effects in the electronic ground state. Unfortunately not all the reports of 27 Al 35 Cl spectra provided data useful for MARVEL. Reddy & Rao (1957)  and  Welz et al. (2006)  contain no assignments, while we failed to obtain a copy of  Ram et al. (1979). The assignments in a number of other sources  (Ram et al. 1982; Mahieu et al. 1989a,b)  provided to be inconsistent not only with other sources but even failed to obey combination differences within themselves. 98SaDiSi  (Saksena et al. 1998)  give reliable, but limited, data for the 𝑎 3 Π -𝑋 1 Σ + band system. They suggest that 82RaRaUpRa  (Ram et al. 1982)  was misassigned and suggested an alternative assignment by shifting 𝐽 values by 3.
or, indeed , in subsequent electronic structure publications. This assumption also does not agree with  Ram et al. (1982). We could not resolve the possible two Λ components structure of a 3 Δ in our analysis. Our decision to treat 𝑏 as 3 Σ + appears to provide satisfactory results. Table  2  gives a summary of the experimental data on Al 35 Cl not included in the MARVEL analysis due to their low resolution, large uncertainties or inconsistent assignment. We use this data in the next paragraph via a 'pseudo-MARVEL' procedure based on their combination differences. Table  3. Example of combination differences for six 𝐴 1 Π -𝑋 1 Σ + transitions from 89MaDuBra  (Mahieu et al. 1989a ), all of which reportedly connect to the same upper state 𝐴 1 Π, 𝑣 ′ = 8, 𝐽 ′ = 29. The lower state energies are taken from the MARVEL set.
05 cm -1. We set a threshold of 0. 3 cm -1 and selected additional 910 pseudo-MARVEL energies to combine them with the pure MARVEL set of 1079 energies. This procedure allowed us to significantly extend the vibrational coverage of the shallow 𝐴 1 Π state using the large sets of transitions provided by 89MaDuBra  (Mahieu et al. 1989a ) and 82RaRuUpRa  (Ram et al. 1982) , up to the last bound state 𝑣 = 10. Vibrationally excited states play important role in refinements as they allow sampling larger ranges of 𝑟 in the potential energy curves. Moreover, with this procedure we were also able to reconstruct the 𝑣 = 0 rovibronic energies of the 𝑏 3 Σ + state up to 𝐽 = 44 using the transitions from 89MaDuBrb  (Mahieu et al. 1989b ) in conjunction with the 𝑣 = 0 energies of 𝑎 3 Π. These 𝑏 3 Σ + 𝑣 = 0 rovibronic states also appear as upper states in the larger experimental sets 82RaRuUpRa and 89MaDuBrb which connect them to a number of 𝑎 3 Π rovibronic states, not only with 𝑣 = 0 (covered in the MARVEL data set), but also to the fundamental and overtone states of 𝑣 = 1, 2, important for refining the shape of the PEC of 𝑏 3 Σ +.
Refinement The model built from our ab initio PECs and couplings was refined using nuclear motion calculations performed with diatomic variational nuclear motion program Duo  (Yurchenko et al. 2016 ). The rovibronic Schrödinger equation was solved for 4 coupled electronic states 𝑋 1 Σ + , 𝐴 1 Π, 𝑎 3 Π and 𝑏 3 Σ + using the methodology described in  Yurchenko et al. (2016). The vibrational basis set was constructed by solving an uncoupled vibrational Schrödinger equations for each of the four electronic states using the Sinc DVR method on an equidistant grid of 500 radial points was used ranging from 0. 7 to 4. 0 Å and comprised 30, 20, 30 and 30 vibrational functions, respectively. The 𝑋 1 Σ + state MARVEL energies with high 𝐽 have large experimental uncertainties and therefore were not strongly constrained in the fit, which is why the corresponding observed minus calculated (obs.
CONCLUSIONS Rovibronic line lists for two main isotopologues of AlCl are presented. These line list, called YNAT, cover both pure bound-bound transitions and bound-quasibound features lying in the continuum; they are available from www. exomol. com  (Tennyson et al. 2020 ) and from www. zenodo. org (European Organization For Nuclear Research & OpenAIRE 2013). The importance for astronomical studies of correctly treating the lifetime broadening which are a feature of quasibound spectra has recently been highlighted by  Yurchenko et al. (2022). We plan to extend our treatment of such spectra to correctly allow for this in future work. Although there have been a number of high resolution studies of studies of the spectrum of AlCl we found serious problems with a studies spanning many of the electronic excited states of this system.
ACKNOWLEDGMENTS This work was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  through  Advance Grant  number  883830 , and by  STFC  Projects No. ST/M001334/1 ,  ST/R000476/1  and  ST/T001429/1. EN thanks  Université Toulouse III -Paul Sabatier  for funding for her visit to  UCL.
DATA AVAILABILITY The states, transition and partition function files for the AlCl YNAT line list can be downloaded from www. exomol. com and the CDS data centre cdsarc. u-strasbg. fr. The open access program Duo and is available via github. com/exomol and from zenodo. During the reviewing process, the YNAT line lists (states, transition, partition function files and Duo inputs) can be downloaded from https://exomol. com/repository/AlCl/YNAT/.
Acknowledgments S. J. R. and A. K. F. are supported by Mayflower studentships from the  University of Southampton. This work is supported by  Investissements d'avenir, Labex ENIGMASS , contrat  ANR-11-LABX-0012. S. F. K. acknowledges the  STFC  Consolidated Grant  ST/L000296/1  and the  European Union 's  Horizon 2020 Research and Innovation programme  under  Marie Skłodowska-Curie grant agreement HIDDeN European ITN project  ( H2020-MSCA-ITN-2019//860881-HIDDeN. This research was supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under grant  396021762 -TRR 257.
This trend can be highlighted when performing a literature search in the PubMed database. In July 2022, 8902 results were found for "(Quantum Dot) and (Cell)" and 4138 results for "(Quantum Dot) and (Animal)". However, before moving towards animal models, most of these studies could have been carried out on 3D in vitro models such as spheroids. A spheroid is a 3D model which recapitulates the structure of small avascular tumors  [11] [12] [13] [14] [15]. By forming a sphere of cancerous cells, certain parameters specific to small avascular tumors can be simulated, such as the existence of oxygen, nutrients, and pH gradients, and the stratification of cells as proliferative periphery, quiescent area, and necrotic core. Furthermore, this model can be complexified by forming cocultured/tricultured spheroids to better represent the impact of the tumor microenvironment and the stromal population.
INTRODUCTION Known genetic markers in complex diseases usually account for only a part of calculated heritability. One possible explanation is that these complex diseases have different subtypes with different genetic factors. The identification of subtypes can nowadays draw upon large heterogeneous datasets, including patient follow-up and genotyping data. When clinical and genomic information is available, subtyping can adopt either of two approaches: 1) the clustering of clinical data with a posterior genetic analysis, or 2) the concomitant clustering of clinical and genomic data. We will discuss the pros and cons of these two approaches in Section 2.
Integrative Clustering In cancer research, a variety of statistical methodologies have emerged for analyzing data coming from different sources, generally multiple omics data, within the field of integrative genomics  (Kristensen et al. , 2014). The philosophy underlying these methodologies is closely related to multi-view learning. Huang et al. (2017)  present a review of multi-omics integration tools. We must also mention mixOmics  (Rohart et al. , 2017) , which proposes various sparse multivariate methods for exploring multiple omics datasets. More specifically, integrative clustering may be built on model-based approaches such as in the representative work by  Shen et al. (2009)  and  Shen et al. (2010). The iCluster method uses a latent variable model to connect multiple data types. The optimization of a penalized loglikelihood involves a process of dimensionality reduction on the representation of the original data that iteratively alternates with several extensions of iCluster using penalties inducing different types of sparsity which have been proposed since  (Shen et al.