dataesr/scientific-paragraphs-categorization huggingface dataset:
For each sample, and each monoexonic transcript, the ratio of reads mapping to the annotated strand over the total reads mapping to the locus was measured. Monoexonic transcripts were retained if they have a ratio of 0. 8 or higher in at least one sample. Third, we applied a strict filter on transcript length; monoexonic transcripts < 500 nt were discarded. Transcripts mapping to unsorted (U), unsorted extra (Uextra) and mitochondrial genome annotations or shorter than 200nt were discarded. The remaining genes were required to have a minimum expression level of 2 reads per kilobase of transcript per million mapped reads (FPKM) in at least one condition as estimated by cuffquant/cuffnorm  [71]  version 2. 2. 1. These transcripts were filtered to remove entries with a high content of repeats or low complexity regions, using repeatMasker soft-masked nucleotides (as in ENSEMBL version 70).
Transcripts containing low complexity or repeated regions for more than 90% of their coverage were discarded. This resulted in 689 retained transcripts (from 497 genes). We further reduced the transcripts by removing redundant transcripts, discarding isoforms differing by just a few nucleotides. For each gene, all pairs of isoforms were compared and measured using the jaccard distance with BEDtools2 (https://github. com/arq5x/ bedtools2) version 2. 22. 1, as previously described  [77]. If a pair of isoforms showed a jaccard distance score above 0,95 then the smallest isoform was removed. The retained transcript set (663 transcripts (from 497 genes) was also scanned to detect potential degradation leftovers of mRNA maturation. For each monoexonic transcript embedded in annotated introns in the same orientation, the expression ratio between nuclear enriched and non-nuclear enriched samples at 3-4h and 6-8h was measured.
Transcripts with a log 2 ratio > 1 were discarded. For this analysis the expression was measured as cuffquant/cuffnorm version 2. 2. 1 FPKMs. The remaining 532 transcripts (367 genes) were manually inspected to detect possible read-through events, resulting in the removal of 53 genes. To define differentially expressed genes, HTSeq-counts version 0. 6. 1 and DESeq2  [72]  version 1. 2. 10 were used (results shown in Figure  S1 ). Genes were considered differentially expressed and added to the ''main set'' of lncRNAs if they had a Benjamini-Hochberg adjusted P-value < 0. 01 in at least one of the following comparisons: The 200 genes (279 transcripts) that failed to show significant differential expression were added to the ''constitutive'' lncRNA group, after passing through two additional filters: First a stringent minimum FPKM filter of higher or equal to 3 reads was applied.
Coding potential analysis The coding capability of our HQ lncRNA gene set, currently annotated lncRNAs and protein coding transcripts was measured using CPAT  [48]  version 1. 2. 1. Following the developers' indications, we took a cut-off value of 0. 39 to assign evidence of coding potential by this method (Figure  1C ). NCBI BLASTX and RPSTBLASTN (version 2. 2. 29+) were run with options -evalue 100 -strand plus -num_alignments 1. We assessed whether a translated product of all possible ORFs matched any annotated proteins in the Drosophila proteome and Swiss-Prot datasets from UniProt (https://www. uniprot. org/) release 2014_08 using BLASTX (Figure  S2B ). RPSTBLASTN was run against the PFAM database from the NCBI's Conserved Domain Database (CDD) FTP-archive (rev. 20 February 2014) to check whether the translated product of the transcripts matched annotated protein domains.
Finally we ran PhyloCSF with options '12flies-orf = ATGStop-frames = 3-aa-removeRefGaps'. Following previous reports  [78] , we used a PhyloCSF score of 20 decibans as threshold, since it allows for a good separation between known coding and non-coding transcripts. All scores for these three tests of coding potential are included in Tables S2 (novel genes) and S3 (annotated genes). The tables also provide a classifier that identifies genes producing transcripts that have values above threshold for two of these tests (CPAT, BLAST searches and PhyloCSF). Improving the annotation of the 5 0 end of the lncRNA by TSS clipping and extension The HQ lncRNA transcription start sites (TSSs) were scanned to identify putative cuffmerge assembly artifacts or alternative TSSs; weakly supported transcripts mapping at the 5 0 end of genuine highly expressed transcripts can be erroneously fused by cuffmerge, falsely expanding the TSS toward the 5 0.
Similarly, alternative TSSs can blur alignments in ChIP-Seq and CAGE analyses. The following approach was applied to post-process cuffmerge assemblies and clip scantly supported TSS: For each HQ novel transcript, the region in the proximity of the predicted TSSs was surveyed to identify CAGE signals supportive of a high quality transcription start site. The region considered in the analysis included up to 800 nt upstream the cuffmerge transcript start, and up to 30% the transcript length moving from the cuffmerge transcript start toward the transcript center. The TSS was redefined within this region to match the closest CAGE peak of at least 50 read if there was a CAGE peak. A total of 71 transcripts exhibited affected 5 0 ends, of which 24 were expanded toward the 5 0 , and 47 reduced in size.
2 Kb. We focused on transcripts that are expressed in the same condition as the ChIP-Seq libraries (cuffnorm gene FPKM > = 1 on Meso 6-8 hr saamples). Figure  1D  shows the mean support of mesodermal promoters (+/À0. 2 Kb) for each library, expressed as normalized and background corrected (Input for Pol II and total H3 for histone modifications) ChIP-Seq read counts. The plots were generated using computeMatrix version 1. 5. 9. 1 of the deeptools suite (https://deeptools. readthedocs. io/en/develop/). 5 0 CAGE mapping and support of transcription start sites CAGE reads from the 10 libraries were demultiplexed and trimmed to obtain the 27nt-tags. We also removed the first nucleotide of the tag, which is frequently an extra G added by the reverse transcriptase, as observed before  [74]. This resulted in 26nt-long tags, which were mapped against the Drosophila melanogaster genome dm3 with bowtie2 version 2.
2. 1) increased by a pseudo-count of 0. 1. lncRNA classification HQ lncRNAs and annotated lncRNA genes were classified with respect to their genome location using the genome annotation (FlyBase version r5. 55) (Figure  3A ) -protein coding genes were considered if expressed in the experimental conditions (resulting from the filtering described in Figure  S2A ). The classification is mutually exclusive and based on the overlap between each gene and several features in the following rank: TSS TES > exon > intron > promoter > enhancer > intergenic. The promoter was defined as the area 1 kb upstream of an annotated TSS. The enhancer set is a superset containing both characterized embryonic enhancers (from transgenic assays) and putative enhancers from TF occupancy, based on the following data: 1) 8008 cis-regulatory modules (CRMs) defined by ChIP for multiple mesodermal transcription factors 2) $4000 Tin bound putative cardiac CRMs  [80] ; 3) CRM activity database 4) RedFly version 3.
ACKNOWLEDGMENTS We are very grateful to all members of the  Furlong lab  for discussions and comments, especially  James Reddington ,  Tim Pollex ,  Olga Mikhaylichenko , and  Nezha Benabdallah. We thank  Guillaume Junion  and  Hilary Gustafson  for generating the twist::EGFP-CBP20 line. This work was technically supported by the  EMBL Genomics ,  Flow Cytometry , and  Advanced Light Microscopy Core Facilities  and the public resources of FlyBase, BDGP, and  RedFly. The work was financially supported by the  FP7 European Research Council (ERC)  grant agreement  322851  ( ERC advanced grant CisRegVar ) to E. E. M. F. ,  EU   FP7 Marie Curie actions  ( 299504 , ncRNAsDevNet) to I. E. S. , EMBL-cofund EIPOD programme to G. B. , and the  BBSRC  ( BB/J01589X/1 ) to A. J. E.
DATA AND SOFTWARE AVAILABILITY The accession number for RNA-seq and CAGE raw data for the twi::EGFP embryos reported in this paper is ArrayExpress: E-MTAB-4069 (total and poly-A+ RNA-seq from) and E-MTAB-4070 (CAGE). The accession number for RNA-seq raw data for the CRISPR KO experiment is European Nucleotide Archive (ENA): ERP110650.
Human myeloma cell lines (HMCLs) XG human myeloma cell lines were obtained as previously described  [15]. JJN3 was kindly provided by Dr. Van Riet (Brussels, Belgium), JIM3 by Dr. MacLennan (Birmingham, UK), and MM1S by Dr. S. Rosen (Chicago, USA). AMO-1, LP1, L363, U266, OPM2, and SKMM2 were purchased from DSMZ (Braunsweig, Germany) and RPMI8226 from ATTC (Rockville, MD, USA). All HMCLs derived in our laboratory were cultured in the presence of recombinant IL-6. HMCLs were authenticated according to their short tandem repeat profiling and their gene expression profiling using Affymetrix U133 plus 2. 0 microarrays deposited in the ArrayExpress public database under accession numbers E-TABM-937 and E-TABM-1088  [15].
Primary multiple myeloma cells Bone marrow samples were collected after patients' written informed consent in accordance with the Declaration of Helsinki and institutional research board approval from Heidelberg and Montpellier University Hospital. Bone marrows were collected from 206 patients treated with high-dose Melphalan (HDM) and autologous stem cell transplantation (ASCT), and this cohort is termed "Heidelberg-Montpellier" (HM) cohort  [16]. Patients' MMCs were purified using anti-CD138 MACS microbeads (Miltenyi Biotec, Bergisch Gladbach, Germany) and their gene expression profile (GEP) obtained using Affymetrix U133 plus 2. 0 microarrays as described  [17]. The CEL files and MAS5 files are available in the ArrayExpress public database (E-MTAB-372). The structural chromosomal aberrations, as well as numerical aberrations, were assayed by fluorescence in situ hybridization (iFISH).
We also used publicly available Affymetrix GEP (Gene Expression Omnibus, accession number GSE2658) of a cohort of 345 purified MMC from previously untreated patients from the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR, USA), termed in the following UAMS-TT2 cohort. These patients were treated with total therapy 2 including HDM and ASCT  [18]. We also used Affymetrix data from total therapy 3 cohort (UAMS-TT3; n = 158; E-TABM-1138)  [19]  of 188 relapsed MM patients subsequently treated with bortezomib (GSE9782) from the study by Mulligan et al. [20].
RNA sequencing HMCLs were cultured for 4 days without or with 1 μM of EPZ6438. RNA samples were collected as previously described. The RNA sequencing (RNA-seq) library preparation was done with 150 ng of input RNA using the Illumina TruSeq Stranded mRNA Library Prep Kit. Paired-end RNA-seq were performed with Illumina NextSeq sequencing instrument (Helixio, Clermont-Ferrand, France). RNA-seq read pairs were mapped to the reference human GRCh37 genome using the STAR aligner  [21]. All statistical analyses were performed with the statistics software R (version 3. 2. 3; available from https://www. r-project. org) and R packages developed by BioConductor project (available from https://www. bioconductor. org/) [22]. The expression level of each gene was summarized and normalized using DESeq2 R/Bioconductor package  [23].
Differential expression analysis was performed using DESeq2 pipeline. P values were adjusted to control the global FDR across all comparisons with the default option of the DESeq2 package. Genes were considered differentially expressed if they had an adjusted P value of 0. 05 and a fold change of 1. 5. Pathway enrichment analyses were performed using online curated gene set collection on the Gene Set Enrichment Analysis software (http://software. broadinstitute. org/gsea/ msigdb/index. jsp) [24, 25].
Gene expression profiling and statistical analyses Gene expression data were normalized with the MAS5 algorithm and analyses processed with GenomicScape (http://www. genomicscape. com) [26]  the R. 2. 10. 1 and Bioconductor version 2. 5 programs  [22]. Gene set expression analysis (GSEA) was used to identify genes and pathways differentially expressed between populations. Univariate and multivariate analysis of genes prognostic for patients' survival was performed using the Cox proportional hazard model. Difference in overall survival between groups of patients was assayed with a log-rank test and survival curves plotted using the Kaplan-Meier method (Maxstat R package)  [27]. The EZ score was built using our previously published methodologies  [28]. EZ score is the sum of the Cox beta-coefficients of each of the 15 EPZ6438-deregulated genes with a prognostic value, weighted by ± 1 if the patient MMC signal for a given gene is above or below the Maxstat reference value of this gene (Table  1 )  [28].
DNA methylation analysis Methylation analysis was performed using the Illumina Infinium HumanMethylation450 BeadChip array (HM450K, Illumina Inc. ). The microarray raw intensities were preprocessed using the R/Bioconductor package minfi  [29]. The methylated CpGs overlapping with genes promoter region were extracted using the R package GenomicFeatures  [30]  implemented in Bioconductor. A CpG is defined as highly methylated when the methylation beta level is above 0. 8.
Ikaros and Aiolos degradation is associated with a downregulation of interferon regulatory factor 4 (IRF4) and MYC which, in turn, reduces MMCs survival  [34]. Investigating the effect of EPZ-6438/lenalidomide combination in XG7 and LP1 MMCs, we found that Ikaros (IKZF1), IRF4, and MYC protein levels were significantly decreased by the combination treatment (65. 5%, 63. 9%, and 14. 8% respectively) compared with lenalidomide (51. 5%, 43% and 2. 2%) or EPZ-6438 (45. 2%, 38. 7%, and 6. 2%) alone (Fig. 5a ). These data were validated by western blot and immunofluorescence (Additional file 1: Figures S10 and S11). Furthermore, EPZ-6438/lenalidomide combination strongly upregulated PAX5, BACH2, and BCL6 B cell transcription factors in association with downregulation of PRDM1 and IRF4 key plasma cell transcription factor (Fig.
The viability and total cell counts were assessed, and the percentage of CD138 viable PC and non-malignant bone marrow cells was determined by flow cytometry. Results are median values of the numbers of myeloma cells in the culture wells. Results were compared with a Wilcoxon test for pairs. c EPZ-6438, lenalidomide, and EPZ-6438/lenalidomide combination deregulated genes in MM. XG7 and LP1 HMCLs were treated with 2 μM lenalidomide (2 days) with (combination) or without (lenalidomide) prior 4 days-treatment with 1 μM EPZ-6438. Venn diagram showing overlap of genes deregulated by EPZ-6438, lenalidomide, or EPZ-6438/lenalidomide combination. d Heatmaps of RNA-seq analysis presenting expression profiles of EPZ-6438, lenalidomide, or combination target genes in XG7 and LP1 HMCLs. e, f Molecular signature of EPZ-6438 and lenalidomide combination deregulated genes compared to control in XG7 and LP1 was investigated using GSEA Database (all curated gene sets), and relevant pathways were presented (FDR q value ≤ 0.
Data are mean values ± standard deviation of three experiments. b Heatmap presenting RNA-seq GEP expression of E2F1, PRDM1, IRF4, MYC, BCL6, CDKN1A, BACH2, and PAX5 in XG7 HMCL treated with EPZ-6438 (1 μM) and/or lenalidomide (2 μM). c XG7 cell cycle was analyzed, after EPZ-6438 (1 μM) and/or lenalidomide (2 μM) treatment, by flow cytometry using DAPI, BrdU incorporation, and labeling with an anti-BrdU antibody. Results are representative of four independent experiments. * indicates a significant difference compared to control cells using a paired t test (P ≤ 0. 05). d The percentage of Ki67 negative cells of EPZ-6438 and/or lenalidomide-treated XG7 HMCL was analyzed by flow cytometry using anti-Ki67 antibody. Data are the mean values ± SD of three separate experiments. * indicates a significant difference compared to control cells using a paired t test (P ≤ 0.
Funding This work was supported by grants from  French INCA (Institut National du Cancer) Institute  ( 2012-109/087437  and  PLBIO15-256 ),  Languedoc Roussillon CRLR  ( R14026FF ),  LR-FEDER Hemodiag, Fondation de France  ( 201400047510 ),  ITMO Cancer (MM&TT) ,  SIRIC Montpellier  ( INCa-DGOS-Inserm 6045 ), the  German Federal Ministry of Education (BMBF) "CAMPSIMM"  ( 01ES1103 ) and within the framework of the e:Med research and funding concept " CLIOM-MICS " ( 01ZX1309 ), and the  Deutsche Forschungsgemeinschaft  ( SFB/TRR79 ; subproject B1). LH is supported by a grant from  Labex EpiGenMed.
Availability of data and materials HMCLs gene expression profiling using Affymetrix U133 plus 2. 0 microarrays are deposited in the ArrayExpress public database under accession numbers E-TABM-937 and E-TABM-1088  [15]. Bone marrows were collected from 206 patients treated with high-dose Melphalan (HDM) and autologous stem  [16]  cell transplantation (ASCT), and this cohort is termed "Heidelberg-Montpellier" (HM) cohort  [16]. Patients' MMCs were purified using anti-CD138 MACS microbeads (Miltenyi Biotec, Bergisch Gladbach, Germany) and their gene expression profile (GEP) obtained using Affymetrix U133 plus 2. 0 microarrays as described  [17]. The CEL files and MAS5 files are available in the ArrayExpress public database (E-MTAB-372). The other datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
75% of the fish species caught by gillnets (62/82) were shown by the metabarcoding study performed on DNA extracted from water samples. eDNA approach also revealed to be sensitive by detecting 30 supplementary species known as present before the dam construction but never caught by gillnets during 3 years. Furthermore, potential of the marker-genes study might be underestimated since it was not possible to assign some sequences at lower taxonomic levels. Although 121 sequences were generated for this study, a third of species in the area, that exhibits high endemism, are still unknown in DNA databases. Efforts to complete local reference libraries must continue to improve the taxonomic assignment quality when using the non-invasive and promising eDNA approach. These results are of broader interest because of increasing number of hydropower projects in the Mekong Basin.
Building of reference sequences databases The ecoPCR program  [26, 43]  was fed with the primer sequences of both markers (Table  1 ) to construct a reference database for the 12S and cytb fragments using all mitochondrial sequences retrieved from Genbank (release 212). Different values of parameters (i. e. e, maximum number of mismatches; minimum (l) or maximum (L) length of the in silico amplified fragment) were tested and the following ones were finally retained: 12S (e = 3, l = 50, L = 150) and cytb (e = 6, l = 150, L = 300). The final size of each reference database was of 13,476 and 75,862 sequences for 12S and cytb respectively. From surface gillnet captures and morphologically assigned fish individuals, 62 sequences of 12S and 59 sequences of cytb were generated for this study in order to improve the number of representatives of endemic fish species of the NT2 area, mostly unknown in molecular databases.
Taxonomic assignment of reads OBITools 1. 2. 9  [44]  is a free-available set of python programs (https://pythonhosted. org/ OBITools/welcome. html) specifically designed for analysing NGS data in a DNA FishCytB-L14912-FusionA-BC2 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TAAGGAGAACGAT-TTCCTAGCCATACAYTAYAC FishCytB-L14912-FusionA-BC3 CCATCTCATCCCTGCGTGTCTCCGACTCAG-AAGAGGATTCGAT-TTCCTAGCCATACAYTAYAC FishCytB-L14912-FusionA-BC4 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TACCAAGATCGAT-TTCCTAGCCATACAYTAYAC FishCytB-L14912-FusionA-BC5 CCATCTCATCCCTGCGTGTCTCCGACTCAG-CAGAAGGAACGAT-TTCCTAGCCATACAYTAYAC FishCytB-L14912-FusionA-BC6 CCATCTCATCCCTGCGTGTCTCCGACTCAG-CTGCAAGTTCGAT-TTCCTAGCCATACAYTAYAC FishCytB-L14912-FusionA-BC7 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TTCGTGATTCGAT-TTCCTAGCCATACAYTAYAC FishCytB-L14912-FusionA-BC8 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TTCCGATAACGAT-TTCCTAGCCATACAYTAYAC FishCytB-L14912-FusionA-BC9 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TGAGCGGAACGAT-TTCCTAGCCATACAYTAYAC FishCytB-L14912-FusionA-BC10 CCATCTCATCCCTGCGTGTCTCCGACTCAG-CTGACCGAACGAT-TTCCTAGCCATACAYTAYAC FishCytB-L14912-FusionA-BC11 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TCCTCGAATCGAT-TTCCTAGCCATACAYTAYAC FishCytB-L14912-FusionA-BC12 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TAGGTGGTTCGAT-TTCCTAGCCATACAYTAYAC FishCytB-H15149-FusionP1 CCTCTCTATGGGCAGTCGGTGAT-GGTGGCKCCTCAGAAGGACATTTGKCCYCA This study (Ion P1 adaptor +primer) 12S Vert-12SV5-F TTAGATACCCCACTATGC (27) 100bp Vert-12SV5-R TAGAACAGGCTCCTCTAG (27) HumBlock-12S-HomoB CTATGCTTAGCCCTAAACCTCAACAGTTAAATCAACAAAACTGCT-SpacerC3 Vert12SV5F-FusionA-BC1 CCATCTCATCCCTGCGTGTCTCCGACTCAG-CTAAGGTAACGAT-TTAGATACCCCACTATGC This study (Ion Torrent A adaptor + barcodes + primer) Vert12SV5F-FusionA-BC2 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TAAGGAGAACGAT-TTAGATACCCCACTATGC Vert12SV5F-FusionA-BC3 CCATCTCATCCCTGCGTGTCTCCGACTCAG-AAGAGGATTCGAT-TTAGATACCCCACTATGC Vert12SV5F-FusionA-BC4 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TACCAAGATCGAT-TTAGATACCCCACTATGC Vert12SV5F-FusionA-BC5 CCATCTCATCCCTGCGTGTCTCCGACTCAG-CAGAAGGAACGAT-TTAGATACCCCACTATGC Vert12SV5F-FusionA-BC6 CCATCTCATCCCTGCGTGTCTCCGACTCAG-CTGCAAGTTCGAT-TTAGATACCCCACTATGC Vert12SV5F-FusionA-BC7 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TTCGTGATTCGAT-TTAGATACCCCACTATGC Vert12SV5F-FusionA-BC8 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TTCCGATAACGAT-TTAGATACCCCACTATGC Vert12SV5F-FusionA-BC9 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TGAGCGGAACGAT-TTAGATACCCCACTATGC Vert12SV5F-FusionA-BC10 CCATCTCATCCCTGCGTGTCTCCGACTCAG-CTGACCGAACGAT-TTAGATACCCCACTATGC Vert12SV5F-FusionA-BC11 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TCCTCGAATCGAT-TTAGATACCCCACTATGC Vert12SV5F-FusionA-BC12 CCATCTCATCCCTGCGTGTCTCCGACTCAG-TAGGTGGTTCGAT-TTAGATACCCCACTATGC Vert12SV5R-FusionP1 CCTCTCTATGGGCAGTCGGTGAT-TAGAACAGGCTCCTCTAG This study (Ion P1 adaptor +primer) https://doi.
org/10. 1371/journal. pone. 0208592. t001 metabarcoding context, in particular for biodiversity surveys from eDNA. Compared to packages for similar purposes like MOTHUR  [45]  or QIIME  [46] , OBITools relies on filtering and sorting algorithms, ultimately allowing to associate a taxonomic information with each sequence record. The OBITools package was demonstrated useful in many recent publications and was used for this survey. In a first analysis, the runs corresponding to filters of C1 and C2 sampling campaigns and two different protocols of DNA extraction were analysed together. In a second analysis, the runs corresponding to the filters of the 5 sampling campaigns (C1, C2, C3, C4 and C5) treated with the second DNA extraction protocol, were analysed together. Roughly the analyses were conducted as follow.
In a preliminary step, the fastq files for all the runs were cleaned (keeping only reads with 90% of bases > Q20 with the Filter_by_quality tool of Galaxy, FASTX-Toolkit, A. Gordon). Then, the files were treated with a succession of OBITools programs. First, the reads for each run were sorted and assigned to samples using barcodes and to markers (12S or cytb) with the ngsfilter program (using the-t option to specify the file containing the samples description). Then, for each marker, files of runs to consider for analysis were concatenated. The obiuniq program was then used to group together identical reads. The number of times each read was observed and in which sample (-m option) are then registered in the new sequence name as attributes. Only sequences for which counts where > or equal to 5 and for which length was comprised between 50bp and 150 bp (12S) or 150bp and 300bp (cytb) were kept with the obigrep program.