dataesr/scientific-paragraphs-categorization huggingface dataset:
Strains of the genus Pyrobaculum were used to test the DNA entry of PSV2 and PFV2 genomes. The samples were maintained at 90°C without shaking for one hour and treated as described above. 16S rRNA gene sequence determination DNA was extracted from exponentially growing cell cultures using the Wizard Genomic DNA Purification Kit (Promega). The 16S rRNA genes were amplified by PCR using 519F (5'-CAGCMGCCGCGGTAA) and 1041R (5'-GGCCATGCACCWCCTCTC) primers  [16]. The identity of the isolated strains was determined by blastn searches queried with the corresponding 16S rRNA gene sequences against the non-redundant nucleotide sequence database at NCBI.
The in silico-translated protein sequences were analyzed by BLASTP  [21]  against the non-redundant protein database at the NCBI with an upper threshold E-value of 1e-03. Searches for distant homologs were performed using HHpred  [22]  against PFAM (Database of Protein Families), PDB (Protein Data Bank) and CDD (Conserved Domains Database) databases. Transmembrane domains were predicted using TMHMM  [23]. CRISPR spacers matching the isolated viruses were searched for in the CRISPRdb by blastn with default parameters  [24].
The comparison was done in the BLASTN mode, with the settings 200/100. The cutoff threshold for non-conserved material was 40%. * -the information according to  [27]. N. D. , not determined. Sampling conducted on October 30, 2018. TMD, transmembrane domains. Blast hits were obtained with BLASTP, using the nr protein database (E-value of 1e-03). Distant homologs were identified by HHpred. Table  S3. Functional annotation of the PFV2 genome. TMD, transmembrane domains. Blast hits were obtained with BLASTP, using the nr protein database (E-value of 1e-03). Distant homologs were identified by HHpred. TMD, transmembrane domains. Blast hits were obtained with BLASTP, using the nr protein database (E-value of 1e-03). Distant homologs were identified by HHpred.
58 9 5516-6424 302 + (9) PSV [YP_015538] 4E-42 35. 05 10 6448-6765 105 - 11 6785-7114 109 + (2) DUF1286-family protein PF06939. 11 99. 5 PSV [YP_015539] 2E-19 47. 86 12 7133-7849 238 + (3) Structural protein VP3 PSV [YP_015540] 3E-12 29. 66 13 7895-8167 90 - 14 8192-8917 241 + (6) Structural protein VP2 PSV [YP_015541] 1E-77 64. 62 15 8919-9821 300 + (6) PSV [YP_015542] 3E-50 40. 22 16 9818-10213 131 + (1) 17 10242-10661 139 + (1) PSV [YP_015544] 9E-17 34. 59 18 10821-11042 73 - PSV [YP_015545] 7E-22 54. 05 19 11042-11509 155 + (4) PSV [YP_015546] 2E-65 63. 92 20 11562-11819 85 - Structural protein VP1 PSV [YP_015547] 2E-25 49. 41 21 11872-12207 111 - 22 12437-12727 96 - 23 12756-13559 267 + (2) PSV [YP_015551] 1E-29 34. 07 24 13556-13699 47 - 25 13731-14138 135 - 26 14135-14740 201 - PSV [YP_015531] 1E-06 32.
1 99. 9 2PY6_A 99 cd02526 90. 2 PFV1 [YP_009237226] 2E-156 PFV1 [YP_009237227] 5E-85 3E-118 ARV2 [YP_009230239] 7E-100 PFV1 [YP_009237255] ARV2 [YP_009230240] 5E-110 98. 60 100. 00 86. 54 97. 60 70. 26 12 13 27 3966-4535 4537-4734 18992-20023 343 189 65 -- - Cas_Cas4 ; DUF83 GDP-mannose-dependent alpha-(1-6)-phosphatidylinositol monomannoside mannosyltransferase PF01930. 17 97. 9 3OKP_A 99. 9 PFV1 [YP_009237228] 1E-136 PFV1 [YP_009237229] 4E-39 ARV2 [YP_009230241] 0. 0 99. 47 100. 00 92. 11 14 15 ORF Position 4727-4918 4915-5118 28 20623-20000 207 63 67 Length, aa TMD Annotation +(2) +(3) -Endoglycosidase; glycoside hydrolase family 18 16 5122-5451 109 -1 35-271 78 -29 21886-20633 417 + (12) Amino acid transporters 17 5453-5920 155 -2 284-460 58 -CopG DNA binding proteins, CopG-like RHH motif 30 22720-21908 270 -Transcription initiation factor IIB PFV1 [YP_009237230] 2E-38 PFV1 [YP_009237231] 1E-40 HHpred hit Probability (%) Blast hit E-value 6Q64_A 88.
7 ARV2 [YP_009230242] 7E-134 PFV1 [YP_009237232] 1E-71 PF00324. 21 99. 9 ARV2 [YP_009230243] 0. 0 ARV2 [YP_009230213] 1E-19 PFV1 [YP_009237233] 4E-108 1IRQ_A 98. 7 SRV [YP_009094260] 3E-23 5IYD_M 100 ARV2 [YP_009230244] 2E-125 Acidianus two-tailed 96. 83 87. 92 Identity (%) 100. 00 46. 67 87. 29 98. 17 99. 35 64. 44 74. 14 18 3 31 19 4 32 20 21 5 33 6 34 5917-6153 955-521 23103-22879 74 78 144 6150-6569 139 1207-992 71 23256-23113 47 6640-7029 129 7026-7463 145 1299-1580 93 23599-23264 111 1582-1962 126 24491-23649 280 ---- -------- HUH superfamily endonuclease, Rep SIRV1 ORF56B, transcription factor PFV1 structural protein VP1 PFV1 structural protein VP2 ParG-like RHH protein with 4 α-helices AcrIII-1 (DUF1874) SIRV1 ORF131 MPN_archaeal; Mov34/MPN/PAD-1 family: archaeal JAB1/MPN/Mov34 metalloenzyme.
This cd08059 2X3G_A 2KEL_B PF09274. 10 94 100 98. 7 PF08960. 10 100 2X5H_D 100 95. 5 ARV2 [YP_009230215] 7E-61 virus [YP_319834] 8E-24 PFV1 [YP_009237234] 1E-52 ARV2 [YP_009230216] 6E-20 ARV2 [YP_009230245] 2E-16 PFV1 [YP_009237235] 5E-95 PFV1 [YP_009237236] 8E-88 PFV1 [YP_009237237] 4E-97 ARV2 [YP_009230246] 1E-51 ARV2 [YP_009230218] 7E-38 ARV2 [YP_009230219] 1E-62 ARV2 [YP_009230247] 4E-171 100. 00 73. 24 56. 76 98. 56 75. 51 72. 34 97. 67 99. 31% 83. 33 68. 47 80. 16 81. 79 TMD, transmembrane domains. Blast hits were obtained with BLASTP, using the nr protein database (E-value of 1e-03). Distant homologs were identified by HHpred.
9 ARV2 [YP_009230241] 0. 0 87. 72 ORF Position 1 16-192 27 20136-19288 282 Length, aa TMD Annotation 58 -CopG DNA binding proteins, CopG-like RHH motif -Transcription initiation factor IIB TMD, transmembrane domains. HHpred hit Probability (%) Blast hit 1IRQ_A 98. 8 5IYD_M 100 ARV2 [YP_009230244] 2E-162 E-value SIRV3 [YP_009272968] 9E-17 Identity (%) 77. 09 62. 07 2 663-217 148 - HUH superfamily endonuclease 2X3G_A 100 ARV2 [YP_009230215] 7E-65 72. 11 3 846-700 48 - Transcription repressor/SIRV1/56B; RHH motif 2KEL_B 98. 9 ARV2 [YP_009230216] 7E-20 78. 72 4 1034-1252 72 - ParG-like RHH protein with 4 α-helices PF09274. 10 95. 8 ARV2 [YP_009230218] 3E-39 84. 72 5 1254-1622 122 - SIRV1 ORF131 2X5H_D 100 ARV2 [YP_009230219] 1E-59 76. 98 6 1623-1895 90 - ARV2 [YP_009230220] 4E-50 81. 11 7 2075-3382 435 - AAA+ ATPase domain PF13337.
19% 24 13156-11741 471 - ARV2 [YP_009230234] 0. 0 79. 62% 25 13355-13594 79 + (2) ARV2 [YP_009230235] 1E-44 88. 61% ORF Position 26 13640-15421 593 Length, aa TMD Annotation -Domain of unknown function (DUF2341) HHpred hit Probability (%) Blast hit PF10102. 9 99. 3 ARV2 [YP_009230236] 0. 0 E-value Identity (%) 77. 53% 1 27 839-1012 15518-18865 1115 57 -- C-terminal Tale-like DNA-binding domain 4HPZ_A 97. 7 ARV2 [YP_009230237] 0. 0 82. 78% 2 28 1039-1410 18867-20078 403 123 -- ARV1 [YP_001542646] 5E-91 39. 80% 3 29 1562-1792 20242-20712 156 76 -- FkbM domain methyltransferase PF05050. 12 99. 1 ARV1 [YP_001542654] 2E-20 ARV2 [YP_009230239] 1E-92 57. 14% 80. 77% 30 20719-21309 196 - Glycosyltransferase like family 2 (Glyco_tranf_2_2) PF10111. 9 89. 4 Acidianus shaped ARV2 [YP_009230240] spindle-virus 1 3E-24 4E-88 78.
earthmicrobiome. org/protocols-and-standards/16s/. Raw reads were processed using the FROGS (Find, Rapidly, Operational taxonomic units (OTUs) with Galaxy Solution) pipeline  [38]  implemented on Galaxy (v. r3. 0-1. 4; Toulouse platform (see https://www. genotoul. fr/)) to generate FASTQ file, perform read quality controls, merge paired-end reads, remove unique singletons, and perform clustering by aggregation. Operational taxonomic units (OTUs) were generated from representative sequences (253 ± 20% bp) clustered as described elsewhere  [38]. Singletons and potential chimeras were then removed to discriminate against sequencing artefacts. This resulted in 2,074,891 paired-end reads, forming, respectively, 14,096 and 11,843 OTUs, for bulk samples and cultures, respectively. Taxonomic affiliations of OTU representative sequences were done against the release 138 of the SILVA SSU database (www.
arb-silva. de) and then double-checked against the RDP Classifier (v. 2. 12). The taxonomic position of all Methanomassiliicoccales-related sequences was further analyzed by integrating the representative sequences into a reference dendrogram, using the distance-based phylogeny reconstruction algorithm BioNJ, as detailed in Text S4. The tree was visualized with iToL (v. 5. 7; https://itol. embl. de/) (Figure  2 ). The 16S rRNA gene amplicons dataset was analyzed using the phyloseq package (v. 1. 28. 0 R)  [39]  on program R studio (v. 1. 3. 5001 ). The OTUs abundance, assigned taxonomy, and sample contextual data were integrated into a phyloseq object before removing potential reagent contaminants using the decontam package (v. 1. 4. 0) [40]. Then, the metagenomSeq package (v. 1. 26. 3) [41]  was used to normalize the dataset of the different samples by cumulative sum scaling (CSS), a median-quantile normalization which corrects differences in library sizes.
The qPCR results were expressed in terms of copy gene numbers per gram of wet weight sample or per ml. For the environmental samples, they were considered as exploitable and biologically relevant when reaching at least 1 × 10 3 gene copies per ml or gram of wet weight of sample. Enrichment slurries targeting hydrogenotrophic methyl-dependent methanogens were performed with environmental samples either (i) having the highest Methanomassiliicoccales 16S rRNA copy number per gram of wet weight or ml quantified (ii) or corresponding to ecosystems with extreme and unusual physical-chemical parameters (KRY150-MgCl 2 -rich brine subjected to more than 33 MPa pressure; KERG-geothermal hot spring). The medium composition was designed based upon (i) previously published recipes used by culture collections (DSMZ medium 141) (Text S5) to grow or enriched Methanomassiliicoccales, (ii) on published enrichment media  [15, 17, 42, 43] , and (iii) on metabolic information derived from metagenome-assembled genomes (MAGs) of Methanomassiliicoccales available in public databases  [27].
Libraries constructions and quality controls were performed by the sequencing facility and verified with FastQC (v. 0. 11. 8-https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). MAGs were reconstructed using the Let-it-bin pipeline (https://github. com/Quent inLetourneur/Let-it-bin). Reads were first trimmed with AlienTrimmer  [47]. Redundant reads were then removed using khmer  [48]. Filtered reads were assembled with Megahit  [49]  and metaSpades  [50]. Binning was performed with MaxBin2  [51] , MetaBAT  [52] , and MetaBAT2  [52]. MAGs assemblies' statistics were obtained with Quast (v. 5. 0. 2; https: //github. com/ablab/quast). MAG completeness and potential contamination were controlled with CheckM (v. 1. 1. 2-https://ecogenomics. github. io/CheckM/) [53]. Methanomassiliicoccales MAGs were identified by phylogenetic approaches described below.
Two Methanomassiliicoccales MAGs obtained from metaSpades and Megahit assemblies contained contigs with identical sequences but interrupted at different loci. In order to improve the contig length, the contigs from these two MAGs were assembled with cap3  [54] , leading to MXMAG1 MAG. A second MAG, MXMAG2, was obtained with metaSpades. Both were analyzed and annotated with the fast annotation software Prokka (v. 1. 14. 6-https://github. com/tseemann/prokka), Dfast (v. 1. 2. 5-https://github. com /nigyta/dfast_core), the online version of the RAST software (v. 2. 0-http://rast. these ed. org/FIG/rast. cgi), and the MicroScope Microbial Genome Annotation and Analysis Platform (MaGe) (https://mage. genoscope. cns. fr/microscope/home/index. php), using KEGG and BioCyc databases with default parameters and databases for all of the five software/pipelines  [55] [56] [57] [58].
ribohost. com/jspeciesws/) [61] , between the high-quality MAG and its closest relative organisms Methanomassiliicoccus luminyensis B10 T (Accession number NZ_CAJE00000000. 1) and Candidatus 'Methanomassiliicoccus intestinalis' Issoire-Mx1 (Accession number: NC_021353. 1). DNA-DNA hybridization (DDH) estimate values were calculated using the genome-to-genome distance calculator (GGDC, v. 2. 1, formula 2)  [62]  between the high-quality MAG and two closest relative organisms cited above. Representative Methanomassiliicoccales genomes from the 'host-associated' and 'freeliving' clades were compared to reconstructed MAGs using the MaGE platform's Pangenome Analysis tool (https://mage. genoscope. cns. fr/microscope/home/index. php), based on the clustering algorithm SiLiX (http://lbbe. univ-lyon1.
fr/-SiLiX-. html) which clustered genomic CDSs by 50% or/and 80% amino-acid identity and 80% amino-acid alignment coverage, with permissive parameters. Resulting CDSs were blasted against the UniprotKB database, and hypothetical protein CDSs were analyzed with the InterProScan webserver (https://www. ebi. ac. uk/interpro/) for functional predictions.
The visualization and statistical analysis of the network at these thresholds was done with Gephi (v. 0. 9. 2)  [65]. Notably, the hierarchical structure was explored through a modularity algorithm  [66]. An in-depth bibliographical analysis of the metabolic characteristics of known representatives of the taxa co-occurring in this network was carried out to focus on potential physiological characteristics described for the cultured taxa of these groups and determine if a putative physiology could be inferred for a given group (Table  S5 ). The connectivity of Methanomassiliicoccales in each of the modules was characterized according to the metabolic characteristics of each taxon belonging to the same module.
Availability of Data and Materials The MAGs' bins generated and analyzed in this study are available under the Whole Genome Shotgun project deposited at DDBJ/ENA/GenBank under the accession num-bers JAEILR000000000 v. 1 (MXMAG1) and JAEILS000000000 v. 1 (MXMAG2). The 16S rRNA gene sequences k119_35148 and k119_91804 are, respectively, available under the accession numbers MW383280 and MW383279. The 16S rRNA gene sequence generated by metabarcoding are available under the accession numbers MW386093-MW386158 in DDBJ/ENA/GenBank databases. In order to explore the distribution of Methanomassiliicoccales associated with terrestrial and aquatic ecosystems a first molecular screening was carried out on 86 anoxic samples collected worldwide. These samples comprised a wide diversity of habitats (marine sediments, deep-sea hydrothermal vents, marine pockmarks, submarine mud volcano, deep-sea hypersaline anoxic brine, peatland soils, lakes, hot springs, river sediments) displaying a large range of physico-chemical conditions (pH, temperature, salinity, pressure).
Diversity and Abundance of Methanomassiliicoccales Metabarcoding analyses confirmed the presence of Methanomassiliicoccales in the 22 samples where the presence of Methanomassiliicoccales was initially detected. The diversity of Methanomassiliicoccales in these samples was low. Indeed, out of the 14,809 prokaryotic OTUs identified by processing the dataset, only 6 OTUs were confirmed to belong to Methanomassiliicoccales based on the automatic assignment made by SILVA 138 database and by finer phylogenetic analyses performed with a set of Methanomassiliicoccales sequences and closely related lineages (Tables  S2  and S3 ). These 6 OTUs (OTUs 21, 30, 101, 926, 137,628, and 137,784) fell into the 'free-living' cluster of the Methanomassiliicoccales (Figure  2 ), in the Methanomassiliicoccaceae family.
Among them, OTU30 was tightly related to M. luminyensis B10 T 16S rRNA sequence (99. 6% sequence similarity) and was present in all of the 22 selected samples, supporting the hypothesis that M. luminyensis might be inferred to a wide range of environmental conditions (Table  1 )  [24, 79]. Eight other OTUs of the dataset were found to belong to very closely related lineages of Methanomassiliicoccales referenced as Ca. 'Lunaplasmatales' and UBA10834  [80]. The total abundance of Methanomassiliicoccales within samples was determined by quantitative PCR (Table  S4 ). For samples where quantification has been possible, abundance of Methanomassiliicoccales was generally very low, ranging from 1. 62 × 10 3 16S rRNA gene copies per gram of wet matrix in MOZ1 samples, to 7. 5 × 10 4 16S rRNA gene copies per gram of wet weight soil in the peatland soil sample MOUG2.
Many samples did not contain Methanomassiliicoccales abundances falling within the range set in this study as being biologically relevant for crude environmental samples (≥1 × 10 3 gene copies per ml or gram of wet weight of sample). These results were congruent with the literature, which indicate that these methanogenic archaea are very scarce in the environment  [14, 23]. However five samples contained non-negligible effectives of Methanomassiliicoccales: (i) soil samples from the Mougau peatland (MOUG1 and MOUG2) with respective abundances of 7. 5 × 10 4 and 6. 2 × 10 4 16S rRNA gene copies of Methanomassiliicoccales per gram of wet weight (and 34. 4% and 1. 7% of total known methanogen's sequences in the metabarcoding dataset, respectively); (ii) sediments from the stream tributary to the Penfeld river (PENF; 5.
Among the Methanomassiliicoccales-affiliated OTUs, sequences belonging to the OTUs 21, 30, 101, and 926 were detected throughout the incubation period and were predominant in the dataset. KRY150). Two others led to methane and ammonium production but without Methanomassiliicoccales enrichment (DOUR and MOUG2) (Figure  S1 ). No methane production or cell growth was detected in the uninoculated control culture. The only culture that showed production of methane associated with an increase in the copy number of Methanomassiliicoccales 16S rRNA sequences over time was the PENF freshwater sediment sample (Figure  3 ). In terms of overall diversity, the species richness slightly decreased over incubation time (from T1 to T10: Shannon index, 3. 11 to 2. 61; Simpson index, 0. 88 to 0. 82) (Figure  S2 ).
This reconstructed genome was relatively streamlined, with coding sequences covering approximately 90. 87% of the entire genome. However, slightly different results were obtained with other annotation software: 2271 CDSs were found with RAST (745/1526 were not integrated to subsystem categories), 2261 CDSs with Prokka, and 2243 CDSs with Dfast. The digital DNA-DNA hybridization (DDH) estimate values between MXMAG1 MAG and M. luminyensis B10 T was 19. 60%, and the one with Ca. 'Methanomassiliicoccus intestinalis' Issoire-Mx1 was 21. 40%, both of which were values well below the standard criterion (70%) for the delineation of a prokaryotic species  [89] , confirming that the MXMAG1 MAG belongs to a different genomic species than M. luminyensis and Ca. 'Methanomassiliicoccus intestinalis'. Thus, we propose that MXMAG1 represents the type material of a novel Methanomassiliicoccus species, for which we propose the name Candidatus 'Methanomassiliicoccus armoricus MXMAG1' (ar.
Annotation with MaGe of Ca. 'Methanomassiliicoccus armoricus MXMAG1' resulted in the prediction of 2448 genomic objects, among which 2407 were protein-coding sequences. This reconstructed genome was relatively streamlined, with coding sequences covering approximately 90. 87% of the entire genome. However, slightly different results were obtained with other annotation software: 2271 CDSs were found with RAST (745/1526 were not integrated to subsystem categories), 2261 CDSs with Prokka, and 2243 CDSs with Dfast. Sequence of MXMAG2 consisted of 203 contigs with an overall size of 1,040,932 bp and a G + C content of 57. 78 mol%. CheckM estimated the genome to be 48. 2% complete based on the presence of single-copy marker genes (82 markers were missing) and with 2. 1% contamination (4 markers were duplicated).
Annotation with MaGe of MXMAG2 resulted in the prediction of 1305 genomic objects, among which 1281 were protein-coding sequences. MXMAG2 had a relatively streamlined genome with coding sequences covering approximately 91. 34% of the entire genome. However, slightly different results were obtained with other annotation software: 1149 CDSs with Prokka and 954 CDSs with Dfast. As for other Methanomassiliicoccales MAGs published previously, both MAGs did not contain one full complete 5S-16S-23S rRNA genes operon  [79]. One and two 5S rRNA genes were found for MXMAG2 and Ca. 'Methanomassiliicoccus armoricus MXMAG1'MAGs, respectively. A partial 542 bp 23S rRNA gene was found in MXMAG2. We detected 24 and 38 tRNA for MXMAG2 and Ca. 'Methanomassiliicoccus armoricus MXMAG1' MAGs, respectively, according to ARAGORN RNA finder (http://130.
The similarity of the 16S rRNA gene sequence with the sequences of the closest cultured relatives, calculated using the "16-based ID" tool provided by the EzBioCloud web server (https://www. ezbiocloud. net/) showed that the sequence k119_35148 (probably the one corresponding to Ca. 'Methanomassiliicoccus armoricus MXMAG1') was quite closely related to M. luminyensis (98. 23% pairwise similarity) and Ca. 'M. intestinalis' (97. 61%), indicating the enrichment of an undescribed representative of Methanomassiliicoccus. This sequence, even if it diverges at a nucleotide base (probably due to a sequencing error in the 16S rRNA gene sequence extracted from the metagenome), most likely corresponds to that of OTU21 present in the metabarcode dataset throughout the PENF cultivation period, and also present in natural samples KERG, MOUG3, MOZ1-3, PAV60, and PENF.
Acknowledgments: The authors thank the co-chiefs, crew and shipboard scientific parties of the oceanographic expeditions that provide samples. We thank  Jean-François Brugère  for the discussions we had at the birth of this project. We are grateful to  Antje Boetius ,  Marc Le Romancer ,  Sabrina Berlendis  and  Telesphore Sime-Ngando , who provided us samples from, respectively, the Håkon Mosby mud volcano, the  Kerguelen archipelago , the  Kryos Basin  and the Pavin lake. We thank  Valérie-Cueff-Gauchard  and  Guillaume Lannuzel  for logistical and technical help with the qPCR. We thank  Xavier Philippon  and  Ludovic Stephan  for technical help with ionic chromatography and GC-FID analyses. LABGeM ( CEA/Genoscope & CNRS UMR8030 ), the  France Génomique and French Bioinformatics Institute  national infrastructures (funded as part of  Investissement d'Avenir program  managed by  Agence Nationale pour la Recherche , contracts  ANR-10-INBS-09  and  ANR-11-INBS-0013 ) are acknowledged for support within the MicroScope annotation platform.
Funding: The study was supported by grants from the  French Ministry of Higher Education and Research , the  Région Bretagne  and the  LabexMer  ( ANR-10-LABX-1 ) for MC. This work was supported by the  program MERLIN Abyss , the " Laboratoire d'Excellence" LabexMER Axis 3 programs ENDIV  and  CULTIVENT  ( ANR-10-LABX-1 ), and the  Sino-French IRP 1211 MicrobSea. Samples from the Mozambic Channel were provided in the frame of the  PAMELA collaborative project (Ifremer/TOTAL SA ).
Molecular analysis JAK2V617F-mutant allele burden was measured as previously described. 9 Molecular profiling of patients was performed on 200 ng of DNA extracted from peripheral blood (QIAcube instrument; Qiagen) using a capture-based custom NGS panel provided by SOPHiA Genetics (Lausanne, Switzerland) targeting 36 myeloid genes involved in MPNs. Genes tested in the panel are as follows: ABL1, ASXL1, BRAF, CALR, CBL, CCND2, CEBPA, CSF3R, CUX1, DNMT3A, ETNK1, ETV6, EZH2, FLT3, HRAS, IDH1, IDH2, IKZF1, JAK2, KIT, KRAS, MPL, NFE2, NPM1, NRAS, PTPN11, RUNX1, SETBP1, SF3B1, SH2B3, SRSF2, TET2, TP53, U2AF1, WT1, and ZRSR2. Bioinformatics analysis was performed by SOPHiA Genetics, and significant variants were identified and retained using SOPHiA DDM software with a detection limit of variants set at 1% for additional mutation.