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Hi,
I am doing a gene expression analysis, I have a raw data of libraries of RNAseq (microRNAs) of 2 conditions and 11 replicates each one, I used multicov to obtain the reads count
bedtools multicov -bams SRR1054203.gz.segemehl.sam.bam.sorted.bam.bam SRR1054204.gz.cutadapt204.cutadapt.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054205.gz.cutadapt205.cutadapt.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054206.gz.cutadapt206.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054207.gz.cutadapt207.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054208.gz.cutadapt208.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054209.gz.cutadapt209.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054210.gz.cutadapt210.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054211.gz.cutadapt211.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054212.gz.cutadapt212.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054218.gz.cutadapt218.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054217.gz.cutadapt217.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054213.gz.cutadapt213.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054214.gz.cutadapt214.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054215.gz.cutadapt215.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054219.gz.cutadapt219.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054220.gz.cutadapt220.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054221.gz.cutadapt221.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054222.gz.cutadapt222.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054223.gz.cutadapt223.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054224.gz.cutadapt224.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054225.gz.cutadapt225.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054226.gz.cutadapt226.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054216.gz.cutadapt216.fastq.segemehl.sam.bam.sorted.bam.bam -bed results.out > conteo_mature_genome2
the result
chr20 62550849 62550871 hsa-mir-941-1 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550905 62550927 hsa-mir-941-2 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550961 62550983 hsa-mir-941-2 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551156 62551178 hsa-mir-941-2 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551268 62551290 hsa-mir-941-2 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550905 62550927 hsa-mir-941-3 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550961 62550983 hsa-mir-941-3 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551156 62551178 hsa-mir-941-3 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551268 62551290 hsa-mir-941-3 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550905 62550927 hsa-mir-941-4 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550961 62550983 hsa-mir-941-4 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551156 62551178 hsa-mir-941-4 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551268 62551290 hsa-mir-941-4 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550905 62550927 hsa-mir-941-5 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550961 62550983 hsa-mir-941-5 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551156 62551178 hsa-mir-941-5 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551268 62551290 hsa-mir-941-5 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
I found for example, several time the mature hsa-miR-941 MIMAT0004984 with different hairpin names and some of them have the same coordinates, and they have the same number of reads count.
I do not know if it possible to have the option of a window to aggregate nucleotides in order to merge read that starts with a few nucleotides of differences, but that belong to the same mature microRNA and report only one microRNA ID.
I check the bedtools manual and have the windowsbed but with multicov it does not work, any idea?. I need to overcome this because when I use this table in edgeR or DESeq, it does not work with repeated ID:
Thanks very much
regards
Adriana
I am doing a gene expression analysis, I have a raw data of libraries of RNAseq (microRNAs) of 2 conditions and 11 replicates each one, I used multicov to obtain the reads count
bedtools multicov -bams SRR1054203.gz.segemehl.sam.bam.sorted.bam.bam SRR1054204.gz.cutadapt204.cutadapt.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054205.gz.cutadapt205.cutadapt.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054206.gz.cutadapt206.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054207.gz.cutadapt207.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054208.gz.cutadapt208.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054209.gz.cutadapt209.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054210.gz.cutadapt210.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054211.gz.cutadapt211.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054212.gz.cutadapt212.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054218.gz.cutadapt218.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054217.gz.cutadapt217.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054213.gz.cutadapt213.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054214.gz.cutadapt214.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054215.gz.cutadapt215.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054219.gz.cutadapt219.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054220.gz.cutadapt220.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054221.gz.cutadapt221.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054222.gz.cutadapt222.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054223.gz.cutadapt223.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054224.gz.cutadapt224.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054225.gz.cutadapt225.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054226.gz.cutadapt226.fastq.segemehl.sam.bam.sorted.bam.bam SRR1054216.gz.cutadapt216.fastq.segemehl.sam.bam.sorted.bam.bam -bed results.out > conteo_mature_genome2
the result
chr20 62550849 62550871 hsa-mir-941-1 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550905 62550927 hsa-mir-941-2 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550961 62550983 hsa-mir-941-2 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551156 62551178 hsa-mir-941-2 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551268 62551290 hsa-mir-941-2 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550905 62550927 hsa-mir-941-3 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550961 62550983 hsa-mir-941-3 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551156 62551178 hsa-mir-941-3 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551268 62551290 hsa-mir-941-3 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550905 62550927 hsa-mir-941-4 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550961 62550983 hsa-mir-941-4 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551156 62551178 hsa-mir-941-4 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551268 62551290 hsa-mir-941-4 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550905 62550927 hsa-mir-941-5 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62550961 62550983 hsa-mir-941-5 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551156 62551178 hsa-mir-941-5 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
chr20 62551268 62551290 hsa-mir-941-5 hsa-miR-941 MIMAT0004984 Homo sapiens miR-941 17 7 12 42 23 41 40 31 28 35 584 45 49 14 53 198 72 7 43 54 37 93 44 37
I found for example, several time the mature hsa-miR-941 MIMAT0004984 with different hairpin names and some of them have the same coordinates, and they have the same number of reads count.
I do not know if it possible to have the option of a window to aggregate nucleotides in order to merge read that starts with a few nucleotides of differences, but that belong to the same mature microRNA and report only one microRNA ID.
I check the bedtools manual and have the windowsbed but with multicov it does not work, any idea?. I need to overcome this because when I use this table in edgeR or DESeq, it does not work with repeated ID:
Thanks very much
regards
Adriana
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