11654151
Description
| Question | Answer |
| Computer Assembly of Genomes | short sequence reads are assembled into a complete genome by: -taking large strings of connected sequences (called contigs) -looking at overlap of sequences obtained -using PCR to fill in gaps between unlinked contigs, and then sequencing gaps *PacBio is making this process automatic!* |
| Annotation | describing the actual contents within a genome sequence |
| Steps of Annotation | 1) Find open reading frames (start with ATG, end with stop codon, usually around 200+ bps long) 2) Find (recognizable) ribosome binding sites upstream of ORFs 3) Determine codon bias of each ORF compared to others 4) predict tRNAs and rRNAs- highly conserved across organism |
| codon bias | each organism prefers some codons for an amino acid over others, creating this bias |
| BLAST | compares your input genomic sequence to all other sequences in a database (NCBI/etc) --> finds HOMOLOGS and looks for protein domains/families/active sites automated by human curation afterwards, each named annotation will have different levels of confidence |
| Basic Bacterial Genome Properties | 1) Size of genome from 150kb to 13Mb 2) Really consistent: 1 ORF per kB 3) 30% of all ORFs in any new genome are annotated as hypothetical 4) Genes are organized in operons (includes 2 or more ORFs) 5) Some genomes contain genomic islands |
| genomic island | large regions of a genome that are unlike the rest; these confer special functions --> different codon bias, absent in closely related species, have direct repeat sequences, encode for functions that aid adaptations to different environments --> think horizontal gene transfer! |
| What are the two main things you can DO with a genome sequence? | 1) Reverse genetics: identify all predicted proteins of a certain type of genome, then knock out each one to assess effect on phenotype --> understand function! 2) Transcriptomics : look at expression of EVERY gene at once, with different environmental conditions or different mutant backgrounds (Ex: RNA-seq) 3) Mass spec-based proteomics: harvest cells growing under different conditions, harvest all proteins --> identify all peptides! 4) Predict metabolic pathways: search BLAST for proteins with known enzymatic activities, connect in pathways -> predict its survival/metabolism in different conditions |
| RNA-seq: methods | start with RNA sample --> treat with DNase I --> deplete of tRNA/rRNA --> perform reverse transcription --> get whole transcriptome of cDNA |
| RNA-seq: data analysis | map short cDNA on their segments of the genome (x = nucleotide position #) higher peaks = more reads = more transcription of a gene 6 ways to make the translational map! --> can use to compare tx in two mutants, under different growth conditions, or in different growth phases |
| Mass spectrometry based proteomics: methods | harvest all proteins of cells grown in different conditions --> digest with trypsin --> separate peptides by HPLC --> vaporize, ionize + accelerate sample in magnetic field magnetic field separates particles based on mass/charge ratio! |
| Mass spectrometry based proteomics: data analysis | compare observed peptide masses to all other predicted peptide masses from an organisms genome --> compile a list of proteins USED under a certain condition, form transcriptomes of that condition and compare to other transcriptome conditions |
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