unfortunatly there is no chromosoem level reference genome available. But there are some genome assemblies available at scaffold or contig level with annotations, tho the wigth have some gaps too. can those be used ?
Hello reader,
we just got some data regarding Macrophage infection analysis with some fungal strains. then rna was extracted from these analysis and sequenced for mRNA 150bpx2.
Human macrophages where used for this analysis. and my colleague extracted rna using the whole well of the assay plate. which means that it has both macrophage and fungus mRNA, no idea in what proportions.
now i want to extract the reads which are fungal specific to use in another genome annotation process.
The idea which i have works like this
- Map QC-cleaned reads to human reference genome using
Hisat2/STAR - Extract all the reads which do not map to human Ref. Using
samtools -f 4flag.
These reads will be theoratically fungal mRNA reads.
Questions:
- is this really the best approach to follow ?
- Which human genome to use for this analysis. Hg38 or T2T genome.
- samtools will only extract reads which show no alignment at all or there should be a threshold to extract reads ?
If you can suggest anyother way which is better that the idea that i have, it will be rally helpful. This data will also be used for RNAseq study but on later stages.
thank you
1 answer
If the genome for the fungus is available then you can use binning approach with bbsplit.sh from BBMap suite. This will allow you to logically address reads that may multi-map to both human/fungal genomes.
It is worth trying. You will know reads that map to the fungal reference (even though fragmented) definitely belong there.
You could also use a transcriptime/cDNA collection instead (if available) since you are working with RNAseq data.
i used the following command. Kindly let me know if i missed something or if it is now correct
bbsplit.sh ref=human_GRCh38.p14_genomic.fna, Fungus_genomic_all_scf.fna \
path=./00.ref_index \
in=./D1_32931/D1_32931_trim_1.fq.gz \
in2=./D1_32931/D1_32931_trim_2.fq.gz \
basename=./01.ref_specific_reads/D1_32931_2_ref_%.fq.gz \
scafstats=./01.ref_specific_reads/D1_32931_2_ref_scafstats.txt \
refstats=./01.ref_specific_reads/D1_32931_2_ref_stats.txt \
ambiguous2=toss -Xmx200g threads=90
and these were the results
------------------ Results ------------------
Genome: 1
Key Length: 13
Max Indel: 20
Minimum Score Ratio: 0.56
Mapping Mode: normal
Reads Used: 105979402 (15849208519 bases)
Mapping: 1964.954 seconds.
Reads/sec: 53934.79
kBases/sec: 8065.94
Pairing data: pct pairs num pairs pct bases num bases
mated pairs: 78.2038% 41439934 78.1535% 12386713356
bad pairs: 7.9484% 4211853 7.9708% 1263314616
insert size avg: 983.99
Read 1 data: pct reads num reads pct bases num bases
mapped: 92.2507% 48883350 92.2429% 7309656727
unambiguous: 83.7040% 44354493 83.7020% 6632841526
ambiguous: 8.5467% 4528857 8.5410% 676815201
low-Q discards: 0.0000% 0 0.0000% 0
perfect best site: 36.4915% 19336720 36.4624% 2889407313
semiperfect site: 36.5918% 19389884 36.5628% 2897361904
rescued: 0.0109% 5776
Match Rate: NA NA 92.5373% 6836944703
Error Rate: 60.3932% 29524867 7.4605% 551204372
Sub Rate: 58.6064% 28651324 3.8982% 288007548
Del Rate: 9.8427% 4811883 1.0646% 78653486
Ins Rate: 24.5991% 12025964 2.4978% 184543338
N Rate: 0.1215% 59402 0.0022% 161138
Read 2 data: pct reads num reads pct bases num bases
mapped: 92.2198% 48867004 92.2110% 7307590567
unambiguous: 83.6751% 44339205 83.6717% 6630864447
ambiguous: 8.5447% 4527799 8.5393% 676726120
low-Q discards: 0.0000% 0 0.0000% 0
perfect best site: 36.2372% 19202003 36.2015% 2868915139
semiperfect site: 36.3385% 19255647 36.3028% 2876942183
rescued: 0.0114% 6044
Match Rate: NA NA 92.5300% 6834312552
Error Rate: 60.6241% 29627809 7.4675% 551555518
Sub Rate: 58.8687% 28769911 3.9199% 289523453
Del Rate: 9.8388% 4808370 1.0623% 78459430
Ins Rate: 24.5342% 11990196 2.4854% 183572635
N Rate: 0.1961% 95825 0.0025% 181927
and stats
#name %unambiguousReads unambiguousMB %ambiguousReads ambiguousMB unambiguousReads ambiguousReads assignedReads assignedBases
human_GRCh38.p14_genomic 96.64158 15318.39947 0.09862 15.676933 102420164 104518 102420164 15318399471
fungi_genomic_all_scf 1.57809 250.129487 0.09862 15.676933 1672452 104518 1776970 265806420
Thank you
Looks good to me. You are being strict with multi-mappers across genomes (i.e. throwing them away). Something to make a note of.
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