Comprehensive Research Tools Guide
1. Bioinformatics & Genomics
Sequence Analysis & Alignment
Basic Local Alignment Search Tool (NCBI)
Use: Compare DNA/protein sequences against databases to find regions of similarity.
Steps: 1) Input sequence in FASTA format, 2) Select appropriate database (nr, RefSeq, etc.), 3) Adjust parameters (E-value, word size), 4) Run search and analyze results.
Steps: 1) Input sequence in FASTA format, 2) Select appropriate database (nr, RefSeq, etc.), 3) Adjust parameters (E-value, word size), 4) Run search and analyze results.
Multiple sequence alignment tool
Use: Create multiple sequence alignments for phylogenetic analysis.
Steps: 1) Upload sequences in FASTA format, 2) Set alignment parameters (iterations, gap penalties), 3) Submit job, 4) View and download alignment in various formats (ALN, PHYLIP).
Steps: 1) Upload sequences in FASTA format, 2) Set alignment parameters (iterations, gap penalties), 3) Submit job, 4) View and download alignment in various formats (ALN, PHYLIP).
Fast multiple sequence alignment
Use: Large-scale sequence alignments with high accuracy.
Steps: 1) Install locally or use web server, 2) Prepare input sequences, 3) Run command:
Steps: 1) Install locally or use web server, 2) Prepare input sequences, 3) Run command:
mafft --auto input.fasta > output.aln, 4) Visualize with alignment viewers.
Short-read alignment for NGS data
Use: Map sequencing reads to reference genomes.
Steps: 1) Index reference genome, 2) Align reads:
Steps: 1) Index reference genome, 2) Align reads:
bowtie2 -x index -1 reads1.fq -2 reads2.fq -S output.sam,
3) Convert SAM to BAM using samtools.
Manipulate NGS data (SAM/BAM/VCF)
Use: Process and analyze aligned sequencing data.
Steps: 1) Convert SAM to BAM:
Steps: 1) Convert SAM to BAM:
samtools view -Sb input.sam > output.bam,
2) Sort BAM file, 3) Index BAM file, 4) Call variants with bcftools.
Genome Assembly & Annotation
Genome assembly tool
Use: Assemble bacterial/viral genomes from NGS reads.
Steps: 1) Install SPAdes, 2) Run:
Steps: 1) Install SPAdes, 2) Run:
spades.py -o output_dir -1 reads_1.fq -2 reads_2.fq --careful,
3) Check assembly metrics (N50, contig count).
Prokaryotic genome annotation
Use: Annotate bacterial genomes (genes, rRNA, tRNA).
Steps: 1) Install Prokka, 2) Run:
Steps: 1) Install Prokka, 2) Run:
prokka genome.fasta --outdir annotation --prefix strain_name,
3) Review annotation files (GBK, GFF).
Eukaryotic gene prediction
Use: Predict protein-coding genes in eukaryotic genomes.
Steps: 1) Install Augustus, 2) Prepare genome sequence, 3) Run prediction:
Steps: 1) Install Augustus, 2) Prepare genome sequence, 3) Run prediction:
augustus --species=human genome.fa > predictions.gff.
Automated microbial genome annotation
Use: Rapid annotation of microbial genomes.
Steps: 1) Create RAST account, 2) Upload genome sequence, 3) Select annotation parameters, 4) Download annotated genome.
Steps: 1) Create RAST account, 2) Upload genome sequence, 3) Select annotation parameters, 4) Download annotated genome.
Variant Analysis & Population Genetics
Genome Analysis Toolkit (variant calling)
Use: Call and filter genetic variants from NGS data.
Steps: 1) Follow GATK Best Practices workflow, 2) Mark duplicates, 3) Base quality recalibration, 4) Variant calling with HaplotypeCaller.
Steps: 1) Follow GATK Best Practices workflow, 2) Mark duplicates, 3) Base quality recalibration, 4) Variant calling with HaplotypeCaller.
GWAS & population genetics
Use: Perform genome-wide association studies (GWAS).
Steps: 1) Prepare input files (PED/MAP or BED/BIM/FAM), 2) Run quality control:
Steps: 1) Prepare input files (PED/MAP or BED/BIM/FAM), 2) Run quality control:
plink --file data --maf 0.05 --mind 0.1 --geno 0.1,
3) Perform association tests.
Annotate genetic variants
Use: Predict functional consequences of variants.
Steps: 1) Input VCF file, 2) Select species and database version, 3) Choose annotation options, 4) Run and download results.
Steps: 1) Input VCF file, 2) Select species and database version, 3) Choose annotation options, 4) Run and download results.
Structural Bioinformatics
3D molecular visualization
Use: Visualize and analyze protein structures.
Steps: 1) Open PDB file, 2) Use commands like
Steps: 1) Open PDB file, 2) Use commands like
show cartoon,
color by chain, 3) Measure distances/angles, 4) Save high-quality images.
Molecular modeling & visualization
Use: Interactive visualization and analysis of molecular structures.
Steps: 1) Load structure file (PDB, mmCIF), 2) Adjust display styles, 3) Perform structural alignments, 4) Create publication-quality figures.
Steps: 1) Load structure file (PDB, mmCIF), 2) Adjust display styles, 3) Perform structural alignments, 4) Create publication-quality figures.
Protein homology modeling
Use: Build 3D protein models from sequence.
Steps: 1) Input target sequence, 2) Select template structures, 3) Generate model, 4) Evaluate model quality (QMEAN, Ramachandran plot).
Steps: 1) Input target sequence, 2) Select template structures, 3) Generate model, 4) Evaluate model quality (QMEAN, Ramachandran plot).
Protein structure prediction
Use: Predict 3D protein structures when templates are unavailable.
Steps: 1) Submit sequence to server, 2) Wait for modeling completion, 3) Download predicted structures, 4) Verify with validation tools.
Steps: 1) Submit sequence to server, 2) Wait for modeling completion, 3) Download predicted structures, 4) Verify with validation tools.
AI-based protein structure prediction
Use: Highly accurate protein structure prediction from sequence.
Steps: 1) Input FASTA sequence, 2) Submit to AlphaFold server, 3) Download PDB file and confidence scores, 4) Visualize in PyMOL/Chimera.
Steps: 1) Input FASTA sequence, 2) Submit to AlphaFold server, 3) Download PDB file and confidence scores, 4) Visualize in PyMOL/Chimera.
Transcriptomics & Epigenomics
Differential gene expression (RNA-seq)
Use: Identify differentially expressed genes from RNA-seq data.
Steps: 1) Import count data, 2) Normalize counts, 3) Perform statistical testing, 4) Visualize results (MA plots, heatmaps).
Steps: 1) Import count data, 2) Normalize counts, 3) Perform statistical testing, 4) Visualize results (MA plots, heatmaps).
RNA-seq alignment
Use: Map RNA-seq reads to reference genome.
Steps: 1) Build genome index, 2) Align reads:
Steps: 1) Build genome index, 2) Align reads:
STAR --genomeDir index --readFilesIn reads.fq --outFileNamePrefix output,
3) Process alignment files for downstream analysis.
ChIP-seq peak calling
Use: Identify transcription factor binding sites.
Steps: 1) Align ChIP-seq reads, 2) Run peak calling:
Steps: 1) Align ChIP-seq reads, 2) Run peak calling:
macs2 callpeak -t chip.bam -c input.bam -n output,
3) Annotate peaks relative to genes.
Single-cell RNA-seq analysis (10x Genomics)
Use: Process 10x Genomics single-cell RNA-seq data.
Steps: 1) Install Cell Ranger, 2) Run:
Steps: 1) Install Cell Ranger, 2) Run:
cellranger count --id=sample --transcriptome=ref --fastqs=fastq_dir,
3) Analyze output with Seurat/Scanpy.
Metagenomics & Microbiome
Microbiome analysis
Use: Analyze 16S rRNA amplicon sequencing data.
Steps: 1) Import sequence data, 2) Perform quality control, 3) Cluster OTUs/ASVs, 4) Calculate diversity metrics, 5) Statistical analysis.
Steps: 1) Import sequence data, 2) Perform quality control, 3) Cluster OTUs/ASVs, 4) Calculate diversity metrics, 5) Statistical analysis.
Taxonomic profiling
Use: Identify microbial composition from metagenomic data.
Steps: 1) Install tool and database, 2) Run classification:
Steps: 1) Install tool and database, 2) Run classification:
metaphlan input.fastq --input_type fastq --nproc 4 > profile.txt,
3) Visualize taxonomic composition.
Metagenomic data analysis
Use: Analyze shotgun metagenomic sequencing data.
Steps: 1) Upload sequences to MG-RAST, 2) Select analysis pipeline, 3) Wait for processing, 4) Explore results via web interface.
Steps: 1) Upload sequences to MG-RAST, 2) Select analysis pipeline, 3) Wait for processing, 4) Explore results via web interface.
2. Drug Design & Computational Chemistry
Molecular Docking & Virtual Screening
Molecular docking
Use: Predict binding modes of small molecules to protein targets.
Steps: 1) Prepare protein (remove water, add hydrogens), 2) Define binding site grid, 3) Prepare ligand (assign charges), 4) Run docking:
Steps: 1) Prepare protein (remove water, add hydrogens), 2) Define binding site grid, 3) Prepare ligand (assign charges), 4) Run docking:
vina --receptor protein.pdbqt --ligand ligand.pdbqt --center_x xx --center_y yy --center_z zz --size_x xx --size_y yy --size_z zz.
Commercial drug discovery platform
Use: Comprehensive drug discovery workflow (docking, MD, QM).
Steps: 1) Prepare protein structure (Protein Preparation Wizard), 2) Generate receptor grid (Glide), 3) Dock ligands (Standard Precision/Extra Precision), 4) Analyze docking poses.
Steps: 1) Prepare protein structure (Protein Preparation Wizard), 2) Generate receptor grid (Glide), 3) Dock ligands (Standard Precision/Extra Precision), 4) Analyze docking poses.
Protein-ligand docking
Use: Flexible ligand and protein docking with genetic algorithm.
Steps: 1) Prepare protein (define binding site), 2) Prepare ligand library, 3) Set genetic algorithm parameters, 4) Run docking, 5) Analyze results (scores, interactions).
Steps: 1) Prepare protein (define binding site), 2) Prepare ligand library, 3) Set genetic algorithm parameters, 4) Run docking, 5) Analyze results (scores, interactions).
Web-based docking
Use: Quick docking predictions without local installation.
Steps: 1) Upload protein structure, 2) Draw/upload ligand, 3) Select docking parameters, 4) Submit job, 5) View results in web interface.
Steps: 1) Upload protein structure, 2) Draw/upload ligand, 3) Select docking parameters, 4) Submit job, 5) View results in web interface.
Molecular Dynamics (MD) Simulations
MD simulation packages
Use: Simulate biomolecular systems at atomic level.
Steps (GROMACS): 1) Prepare topology (
Steps (GROMACS): 1) Prepare topology (
gmx pdb2gmx),
2) Define simulation box, 3) Solvate system, 4) Add ions,
5) Energy minimization, 6) Equilibration, 7) Production MD.
GPU-accelerated MD
Use: High-performance molecular simulations.
Steps: 1) Prepare system (PDB, PSF files), 2) Set up simulation (force field, integrator), 3) Run on GPU:
Steps: 1) Prepare system (PDB, PSF files), 2) Set up simulation (force field, integrator), 3) Run on GPU:
python simulate.py,
4) Analyze trajectories.
Force fields & simulations
Use: All-atom simulations with CHARMM force field.
Steps: 1) Build system (protein, membrane, etc.), 2) Apply force field parameters, 3) Minimize energy, 4) Run dynamics (NVT/NPT ensembles), 5) Analyze results.
Steps: 1) Build system (protein, membrane, etc.), 2) Apply force field parameters, 3) Minimize energy, 4) Run dynamics (NVT/NPT ensembles), 5) Analyze results.
Pharmacophore Modeling & QSAR
Pharmacophore modeling
Use: Create 3D pharmacophore models from protein-ligand complexes.
Steps: 1) Load PDB complex, 2) Generate pharmacophore features, 3) Refine model, 4) Use for virtual screening.
Steps: 1) Load PDB complex, 2) Generate pharmacophore features, 3) Refine model, 4) Use for virtual screening.
Commercial drug design software
Use: Comprehensive computational chemistry platform.
Steps: 1) Build/import molecules, 2) Perform docking (MOE Dock), 3) QSAR modeling, 4) Analyze protein-ligand interactions.
Steps: 1) Build/import molecules, 2) Perform docking (MOE Dock), 3) QSAR modeling, 4) Analyze protein-ligand interactions.
Cheminformatics toolkits
Use: Manipulate chemical structures and compute descriptors.
Steps (RDKit): 1) Install Python package, 2) Read molecules:
Steps (RDKit): 1) Install Python package, 2) Read molecules:
from rdkit import Chem; mol = Chem.MolFromSmiles('CCCO'),
3) Calculate properties, 4) Generate fingerprints.
ADMET Prediction
Drug-likeness prediction
Use: Predict pharmacokinetic properties of small molecules.
Steps: 1) Draw/upload molecule, 2) Submit for analysis, 3) View results (Lipinski's rule of 5, solubility, etc.).
Steps: 1) Draw/upload molecule, 2) Submit for analysis, 3) View results (Lipinski's rule of 5, solubility, etc.).
Toxicity & ADMET profiling
Use: Predict absorption, distribution, metabolism, excretion, toxicity.
Steps: 1) Input SMILES or draw structure, 2) Select prediction models, 3) Submit, 4) Download comprehensive ADMET report.
Steps: 1) Input SMILES or draw structure, 2) Select prediction models, 3) Submit, 4) Download comprehensive ADMET report.
Toxicity prediction
Use: Predict various toxicity endpoints.
Steps: 1) Input molecule (name, SMILES, or draw), 2) Run prediction, 3) View toxicity classes and targets.
Steps: 1) Input molecule (name, SMILES, or draw), 2) Run prediction, 3) View toxicity classes and targets.
3. Neurobiology & Neuroscience
Neuroimaging & Brain Mapping
fMRI analysis
Use: Analyze functional and structural MRI data.
Steps: 1) Preprocess data (motion correction, registration), 2) First-level analysis (FEAT), 3) Higher-level statistics, 4) Visualize results (FSLeyes).
Steps: 1) Preprocess data (motion correction, registration), 2) First-level analysis (FEAT), 3) Higher-level statistics, 4) Visualize results (FSLeyes).
MATLAB-based neuroimaging
Use: Statistical analysis of brain imaging data.
Steps: 1) Preprocess (realign, normalize, smooth), 2) Specify statistical model, 3) Estimate model, 4) View statistical maps (p-values, t-scores).
Steps: 1) Preprocess (realign, normalize, smooth), 2) Specify statistical model, 3) Estimate model, 4) View statistical maps (p-values, t-scores).
Functional MRI processing
Use: Process and analyze fMRI data.
Steps: 1) Slice timing correction, 2) Motion correction, 3) Spatial normalization, 4) Statistical analysis (3dDeconvolve).
Steps: 1) Slice timing correction, 2) Motion correction, 3) Spatial normalization, 4) Statistical analysis (3dDeconvolve).
Brain cortical reconstruction
Use: Analyze and visualize brain anatomy.
Steps: 1) Run recon-all pipeline on T1 image, 2) Inspect results (white matter segmentation), 3) Perform cortical thickness analysis.
Steps: 1) Run recon-all pipeline on T1 image, 2) Inspect results (white matter segmentation), 3) Perform cortical thickness analysis.
Electrophysiology & Spike Sorting
Open-source electrophysiology
Use: Acquire and analyze neural electrophysiology data.
Steps: 1) Set up acquisition hardware, 2) Configure signal processing pipeline, 3) Record data, 4) Export for analysis.
Steps: 1) Set up acquisition hardware, 2) Configure signal processing pipeline, 3) Record data, 4) Export for analysis.
Spike sorting
Use: Cluster neural spikes from extracellular recordings.
Steps: 1) Preprocess raw data (filter, detect spikes), 2) Run clustering algorithm, 3) Manually curate clusters, 4) Export spike times.
Steps: 1) Preprocess raw data (filter, detect spikes), 2) Run clustering algorithm, 3) Manually curate clusters, 4) Export spike times.
Neural network modeling
Use: Simulate biologically realistic neurons/networks.
Steps (NEURON): 1) Define morphology and biophysics, 2) Insert channels, 3) Set up stimulation, 4) Run simulation, 5) Analyze voltage traces.
Steps (NEURON): 1) Define morphology and biophysics, 2) Insert channels, 3) Set up stimulation, 4) Run simulation, 5) Analyze voltage traces.
Connectomics & Network Analysis
Network analysis
Use: Analyze brain connectivity networks.
Steps: 1) Create adjacency matrix from imaging data, 2) Compute graph metrics (clustering, path length), 3) Compare groups/conditions.
Steps: 1) Create adjacency matrix from imaging data, 2) Compute graph metrics (clustering, path length), 3) Compare groups/conditions.
Diffusion MRI tractography
Use: Reconstruct white matter pathways.
Steps: 1) Preprocess DWI data (eddy current correction), 2) Estimate fiber orientations, 3) Run tractography, 4) Visualize/quantify tracts.
Steps: 1) Preprocess DWI data (eddy current correction), 2) Estimate fiber orientations, 3) Run tractography, 4) Visualize/quantify tracts.
Behavioral Analysis
Pose estimation (animal behavior)
Use: Track animal body parts in videos.
Steps: 1) Label frames to create training set, 2) Train deep neural network, 3) Analyze new videos, 4) Extract movement kinematics.
Steps: 1) Label frames to create training set, 2) Train deep neural network, 3) Analyze new videos, 4) Extract movement kinematics.
Behavioral observation software
Use: Code and analyze animal/human behavior.
Steps: 1) Define ethogram (behavioral categories), 2) Annotate videos, 3) Export time-stamped events, 4) Calculate behavioral statistics.
Steps: 1) Define ethogram (behavioral categories), 2) Annotate videos, 3) Export time-stamped events, 4) Calculate behavioral statistics.