Admin

For 120 protein sequences, MUSCLE5 and MAFFT are both excellent choices. MUSCLE5 is often more accurate; MAFFT is faster for very large datasets (>1000 sequences).…

Here is the complete QIIME2 workflow for paired-end 16S data: **1. Import reads** ```bash qiime tools import --type 'SampleData[PairedEndSequencesWithQuality]' --input-path manifest.csv --input-format PairedEndFastqManifestPhred33V2 --output-path demux.qza…

You need to first create the BLAST database using `makeblastdb` before you can query it. Here's the full workflow: ```python from Bio.Blast.Applications import NcbimakeblastdbCommandline, NcbiblastpCommandline…

Use **cyvcf2** — it's ~20x faster than PyVCF because it wraps htslib in C: ```python from cyvcf2 import VCF import numpy as np vcf =…

BioPython doesn't have a built-in pairwise identity function for MSAs, but it's easy to write one: ```python from Bio import AlignIO import numpy as np…

For bacterial genomes with Flye and Nanopore reads, you generally want **30–60x coverage** for a good assembly. 15x is too low and explains the fragmentation.…

**Do NOT pre-normalize your counts before DESeq2.** DESeq2 expects raw integer counts and does its own normalization internally using the median-of-ratios method. ```r library(DESeq2) #…

Harmony is a good choice. Here's the correct workflow: ```r library(Seurat) library(harmony) # Merge your objects combined