Gene Expression Analysis and Machine Learning in Bioinformatics

Gene expression analysis

Study of gene activity in various conditions.

Differential expression

Comparison of gene expression levels across conditions.

Transcriptome

Complete set of expressed genes in a cell.

Unsupervised learning

Learning without labeled output data.

Over-represented terms

Terms occurring more frequently than expected.

Supervised learning

Learning with labeled output data.

K-nearest neighbor

Classification based on closest training examples.

Logistic regression

Statistical method for binary classification.

Neural networks

Computational models inspired by brain structure.

Random forest

Ensemble method using multiple decision trees.

Support vector machine

Classification method maximizing margin between classes.

Gene expression variability

Differences in gene expression across samples.

Technical replicates

Repeated measurements to control experimental variation.

Biological replicates

Independent samples to account for biological variation.

RNA-Seq

Sequencing method for analyzing RNA expression.

Differentially expressed genes (DEG)

Genes with significant expression changes between conditions.

Log fold change (logFC)

Measure of relative change in gene expression.

P-value

Probability measure for statistical significance.

False discovery rate (FDR)

Proportion of false positives among significant results.

De novo transcriptome assembly

Building transcriptomes without a reference genome.

Burrows-Wheeler transform

Algorithm for efficient sequence mapping.

Housekeeping genes

Genes consistently expressed across all conditions.

Transcripts per million (TPM)

Normalization method for RNA-Seq data.

Reads per kilobase of gene per million reads (RPKM)

Normalization accounting for gene length and sequencing depth.

Batch effects

Variability introduced by processing batches of samples.

Condition variability

Differences caused by environmental factors.

Single cell approaches

Techniques analyzing gene expression at single-cell level.

Diurnal variation

Daily fluctuations in gene expression.

Seasonal variation

Changes in gene expression across seasons.

Quality control (QC)

Processes ensuring data accuracy and reliability.

Microarrays

Techniques for measuring gene expression variations.

Biological Replicates

Samples to control for biological variation.

Internal Standards

Used to determine absolute RNA levels.

Housekeeping Genes

Implicit internal standards for RNA quantification.

Spiked RNA

Explicit internal standards added for measurement.

RNA Library Preparation

Process to isolate desired RNA types.

PolyA+ RNA

Messenger RNA enriched for sequencing.

Ribosomal RNA

Most abundant RNA type, usually subtracted.

Next-Generation Sequencing

High-throughput sequencing technology for RNA-Seq.

Read Mapping

Aligning sequenced reads to a reference genome.

Gene Expression Variation

Expression levels can vary over 5 orders of magnitude.

Fast Mapping Methods

Efficient algorithms for read alignment.

Burrows-Wheeler Transform

Algorithm used for fast read mapping.

Differentially Expressed Genes (DEG)

Genes with significant expression differences.

Log Fold Change (logFC)

Biological effect size metric for gene expression.

False Discovery Rate (FDR)

Proportion of false positives among significant DEGs.

Volcano Plots

Visual representation of fold change and significance.

T-test

Statistical test for comparing gene expression means.

Wald Test

Test for significant differences in log fold change.

RNA Isoforms

Transcripts differing due to splicing variations.

Quantitative PCR (qRT-PCR)

Method for confirming gene expression results.

Noise in Gene Expression

Variability affecting measurement accuracy.

Library Size

Total number of transcripts captured in sequencing.

Reads per Kilobase per Million (RPKM)

Normalization for gene length and sequencing depth.

Contaminants in RNA-Seq

Unwanted RNA affecting expression analysis.

Isoform Estimation Challenges

Difficulties in accurately measuring RNA isoforms.

Single Cell RNA-Seq

Technique to analyze gene expression in individual cells.

FACS

Fluorescence-activated cell sorting for cell separation.

Drop-seq

Method for sequencing RNA from single cells.

ChIP-seq

Chromatin immunoprecipitation followed by sequencing.

Methylation Sequencing

Identifies methylated DNA regions using bisulfite treatment.

Hi-C

Technique for studying chromosome conformation.

ATAC-seq

Assesses chromatin accessibility using transposase.

MNase-seq

Uses micrococcal nuclease to study nucleosome positioning.

Co-expressed Genes

Genes responding similarly to treatments or conditions.

Clustering

Grouping similar data points based on characteristics.

Hierarchical Clustering

Creates a tree of clusters based on distance.

Euclidean Distance

Measures straight-line distance between two points.

Correlation Distance

Measures similarity based on response patterns.

UPGMA

Average linkage clustering method for hierarchical trees.

Principal Component Analysis (PCA)

Reduces dimensionality by transforming data axes.

Eigenvectors

New axes representing directions of maximum variance.

Eigenvalues

Weights indicating variance explained by eigenvectors.

Principal Coordinate Analysis (PCoA)

Visualizes data based on distance metrics.

Heatmaps

Visual representation of gene expression data.

K-means Clustering

Partitions data into K groups based on means.

Transcriptional Regulatory Networks

Networks of genes regulated by transcription factors.

Histone Modifications

Chemical changes to histones affecting gene expression.

Biomarkers

Biological indicators of disease or condition.

Diagnostics

Methods for identifying diseases or conditions.

Log-transformed Values

Data transformation for scale normalization in analysis.

Sjögren's Syndrome

Autoimmune disease affecting moisture-producing glands.

Acute Myeloid Leukemia (AML)

Type of cancer affecting blood and bone marrow.

Huntington's Disease

Genetic disorder causing progressive brain degeneration.

K-means clustering

A method to partition data into K clusters.

Choosing K

Determining the optimal number of clusters.

Euclidean distance

Distance metric for measuring point separation.

Within group error

Sum of distances within a cluster.

Between group distance

Distance between different clusters.

Silhouette statistic

Measure of how similar an object is to its cluster.

Self-organizing map (SOM)

Neural network for clustering and visualization.

Grid connectivity

Arrangement of centroids in self-organizing maps.

Biclustering

Finding gene sets associated with specific classes.

Mean squared residue score (H)

Metric for evaluating cluster homogeneity.

Brute force algorithm

Exhaustive search for optimal clustering.

Gene Ontology (GO)

Framework for classifying gene functions.

Fisher's exact test

Statistical test for assessing subset bias.

Hypergeometric distribution

Probability distribution for sampling without replacement.

Gene ontology enrichment

Analysis of common GO terms in clusters.

Pathway enrichment

Assessing gene association with biological pathways.