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Shubhi Bindal
Shubhi Bindal @BindalShubhi ยท
Seeking guidance from the bioinformatics community ๐Ÿงฌ Background:M.Sc9. Biochemistry Currently transitioning into computational biology Goal: PhD or industry role What should I focus on next to stand out? #Bioinformatics #PhDLife #CareerAdvice
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Dr. Noble K Kurian ๐Ÿ‡ฎ๐Ÿ‡ณ
Dr. Noble K Kurian ๐Ÿ‡ฎ๐Ÿ‡ณ @DrNobleKKurian ยท
๐—ฆ๐—ง๐—”๐—ฅ๐—ง๐—œ๐—ก๐—š ๐—œ๐—ก ๐Ÿฎ ๐——๐—”๐—ฌ๐—ฆ.. ๐—ข๐—ก๐—Ÿ๐—ฌ ๐—Ÿ๐—œ๐— ๐—œ๐—ง๐—˜๐—— ๐—ฆ๐—˜๐—”๐—ง๐—ฆ ๐—Ÿ๐—˜๐—™๐—ง..๐—๐—ข๐—œ๐—ก ๐—ก๐—ข๐—ช.. ๐Ÿš€ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๏ฟฝlnkd.in/gNqiwkJi๐—ก๐—”-๐˜€๐—ฒ๐—พ ๐——๐—ฎ๏ฟฝ๏ฟฝ๐—ฎ ๐—”๐—ป๐—ฎ๐—น๏ฟฝ๏ฟฝ๐˜€๐—ถ๐˜€ ๐Ÿ”— ๐—ฅ๐—ฒ๐—ด๏ฟฝs://t.co/7YeHC1a84g #Bioinformatics #SingleCell #RNASeq #Research https://t.co/RLqGV95EFM
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BIOINFORANGE
BIOINFORANGE @bioinforange ยท
BIOINFOCONGRESS VIII iรงin bilgilendirme mailleri gรถnderildi ๐Ÿ“ฉ 28โ€“29 Mart 2026โ€™da gerรงekleลŸecek kongremiz รถncesinde, kayฤฑtlฤฑ e-posta adreslerinizi ve spam klasรถrรผnรผzรผ kontrol etmeyi unutmayฤฑn! #congress #biology #bioinformatics #bioinforangeu
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Kevin Aguirre
Kevin Aguirre @KevinAg21289665 ยท
I was featured in the โ€œFirst Personโ€ series by Biology Open ๐ŸŽ‰ I share my journey into bioinformatics and my research on genome evolution & horizontal gene transfer using computational approaches. ๏ฟฝjournals.biologists.com/bio/article/15โ€ฆrT #Bioinformatics #Genomics #AcademicTwitter
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First person โ€“ Kevin Aguirre-Carvajal

ABSTRACT. First Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping researchers promote themselves alongside their papers. Kevin...

From journals.biologists.com
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Finch
Finch @finch_shenq ยท
Working on a non-model genome and need help? - Repeat library construction - Multi-evidence gene prediction - RNA-seq guided annotation - BUSCO QC at every step I build custom pipelines. DMs open. #Bioinformatics #Genomics
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Finch
Finch @finch_shenq ยท
My genome annotation QC checklist: 1. BUSCO protein-mode > 90% 2. Gene count in expected range 3. Average gene length reasonable 4. Exon/intron ratio consistent 5. No assembly artifacts in models 6. Functional annotation > 70% What would you add? #Bioinformatics #Genomics
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Finch
Finch @finch_shenq ยท
BUSCO tip most people miss: Genome-mode (98.1%) != Protein-mode (93.2%) Genome-mode includes internal stop codons and pseudogenes. Protein-mode reflects actual coding potential. Always report BOTH for a complete picture. #Bioinformatics #BUSCO #QC
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Finch
Finch @finch_shenq ยท
Counterintuitive finding: Stricter EVM filtering DECREASED BUSCO (81.2% to 79.5%). Why? Aggressive filtering removes real genes with weak evidence in non-model species. Solution: Keep lenient EVM output, rescue with ortholog validation. #Bioinformatics #GenomeAnnotation
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Finch
Finch @finch_shenq ยท
Used 6 RNA-seq libraries from different Litsea tissues: HISAT2 > StringTie > merged GTF > TransDecoder Found 4,194 novel genes not predicted by protein methods alone. RNA-seq is irreplaceable for species-specific gene discovery. #Bioinformatics #RNAseq
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Finch
Finch @finch_shenq ยท
miniprot mapped 28K+ proteins to a 1.33 Gb genome in just 5 minutes (64 threads). Peak RAM: 23 GB Result: 92.5% BUSCO from protein evidence alone For non-model species, miniprot is a game-changer for structural annotation. #Bioinformatics #Genomics
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Finch
Finch @finch_shenq ยท
Repeat masking a 1.33 Gb plant genome: RepeatModeler: 9h 14min to build custom library RepeatMasker: 66.8% masked Tip: LTR_retriever can fail silently. Always check logs - missing LTR families = underestimating repeat content. #Bioinformatics #Genomics
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Finch
Finch @finch_shenq ยท
BUSCO progression in our pipeline: EVM raw: 81.2% +filtering: 79.5% +miniprot: 92.5% +merged: 93.2% Genome: 98.1% EVM alone misses ~12% of conserved genes. Protein alignment rescue is essential. #Bioinformatics #GenomeAnnotation
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Oriaku Ikemefula Solomon
Oriaku Ikemefula Solomon @gentle_iyke ยท
Whole Exome Sequencing does not end at the lab. FastQC => BWA => GATK => ANNOVAR. Quality, alignment, deduplication, variant calling, filtering, annotation. Every step is a gate. Bad pipeline = bad science. #Bioinformatics #GrowWithPitchIn
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