Deciphering the mysteries of human genomes

This keynote was presented at EuroPython 2024 in Prague, Czech Republic.

Abstract

Have you ever wondered why we are the way we are? Why some individuals develop diseases while others remain healthy? And what does Python have to do with all of this? Join this talk in which we will explore the interface between biology, technology and medicine, in the context of the research of rare genetic diseases. Learn what the Moore’s law has to do with advances in genetics and medicine, or why bigger is not always better.

Transcript

1. Deciphering the mysteries of human genomes Anna Přistoupilová EuroPython 10th July 2024 https://www.linkedin.com/in/pristanna/
2. https://microbenotes.com/bioinformatics-introduction-and-applications/
3. BIOLOGY AND INTRO TO GENETICS Fascinating inner life of the cell
4. Human genome https://www.genome.gov/genetics-glossary/Genome
5. Chromosomes https://www.genome.gov/genetics-glossary/Genome
6. DNA A-T C-G https://www.genome.gov/about-genomics/educational-resources/fact-sheets/human-genomic-variation
7. Size of human genome – 3 x 109 bp https://www.verbatim-europe.com/files/products/dvdplusr-16x-matt-silver/dvdr-matt-silver-16x.png
9. Size of human genome https://neherlab.org/20180920_theoretical_biophysics.html
10. https://courses.lumenlearning.com/suny-microbiology/chapter/structure-and-function-of-cellular-genomes/1000/
11. Genetic code http://hyperphysics.phy-astr.gsu.edu/hbase/Organic/gencode.html https://www.thetech.org/ask-a-geneticist/articles/2022/why-three-base-codon/
12. https://learngenomics.dev/docs/biological-foundations/the-central-dogma/
13. https://cdn.rcsb.org/pdb101/molecular-machinery/
14. To see the video, follow the URL below https://archive.org/details/vimeo-90405549
15. RARE DISEASES How rare are rare diseases?
17. What happens when there is a change in the code? • Nothing happens • Differences between individuals • Disease – Error – Performance Issues • Death – Out of space – Out of memory = death
18. T>C
19. Genetic code http://hyperphysics.phy-astr.gsu.edu/hbase/Organic/gencode.html https://bio.libretexts.org/Courses/Clinton_College/BIO_300%3A_Introduction_to_Genetics_%28Neely %29/03%3A_Mutation/3.02%3A_Consequences_of_mutations
20. Effect size Variant frequency (%) low intermediate very rare high modest common Rare diseases Beneficial alleles Modifying factors Oligogenic or polygenic diseases Lethal
21. Rare diseases https://www.rarediseaseday.org/
22. Examples of rare diseases • Cystic fibrosis • Huntington's disease • Phenylketonuria (PKU) • Spinal muscular atrophy (SMN1) • Amyotrophic lateral sclerosis (ALS) • Duchenne muscular dystrophy • Homocystinuria (HCU) • Gaucher disease • Niemann-Pick disease • Pompe disease • Tay-Sachs disease • …… https://rarediseases.org/rare-diseases/ https://en.wikipedia.org/wiki/Ice_Bucket_Challenge
23. Rare diseases • Affect people of ALL AGES • Are PROGRESSIVE – worsening over time, leading to death • Can affect ANY PART of the body • Wide range of SYMPTOMS – physical, neurological, develpomental, behavioral • Genetic CAUSE is UNKNOWN for many • Patients are often UNDIAGNOSED for years or decades • Social and emotional impact on the patient and their families - feelings of isolation, depression, and anxiety.
24. Why to study rare diseases • Help patients with rare diseases – Treatment • 5% or fewer of rare diseases are estimated to have at least one approved treatment • Diagnose early! – Diagnosis – Family planning – Peace in mind • Are not so rare! • Extend knowledge about human biology and help other patients – Model for the study of cell physiology and patophysiology – New insights into biological function of disease-causing genes – Better understanding of complex diseases
25. DNA SEQUENCING Universal method to study rare diseases
26. Sanger Sequencing – 1st gen Next Generation Sequencing (NGS) – 3rd gen Next Generation Sequencing (NGS) – 2nd gen
27. https://healthcocreation.files.wordpress.com/2014/02/diapositiva1.jpg Sanger sequencing NGS sequencing
28. NGS – 2nd generation https://theory.labster.com/fragmentation/
29. NGS – 2nd generation 100 bp https://theory.labster.com/fragmentation/
30. NGS – 2nd generation - Illumina
31. What’s that?
32. WHAAA! London, 2017
33. NGS – 3rd generation https://nanoporetech.com/ WHAAA!
34. kbp-Mbp NGS – 3rd generation
35. kbp-Mbp NGS – 3rd generation
36. van Dijk, E. L., Jaszczyszyn, Y., Naquin, D., & Thermes, C. (2018). The Third Revolution in Sequencing Technology. Trends in Genetics, 34(9), 666–681. https://doi.org/10.1016/j.tig.2018.05.008
38. https://astrobiology.com/2024/02/offworld-genomics-research-on-the-iss-studying-dna-in-space.html
39. BIOINFORMATICS ANALYSIS Map  Call Variants  Annotate  Interpret
40. http://biocomicals.blogspot.com Bioinformatics
41. Modified from https://www.my46.org/sites/www.my46.org/files/pictures/11My46_Gen101_Exome_final.png Genome size Raw data size 150 Gb 20 Gb 1 Gb Whole Genome (WGS) Whole Exome (WES) 2% Targeted Seq
42. @SEQ_ID GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAA + !''*((((***+))%%%++)(%%%%).1***-+*'') >SEQ_ID GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAA Formats for storing seqeuences FASTA FASTQ https://en.wikipedia.org/wiki/FASTQ_format
43. Read mapping • You have the box – reference genome • Align the reads to the reference genome Image from https://en.wikipedia.org/wiki/Genomics … is like a jigsaw puzzle … • Missing pieces (coverage bias) • Broken pieces (sequencing errors) • Duplicate pieces (repeats) • Pieces from another puzzle (contamination)  Some pieces easier to place than others
44. https://bioinformaticamente.com/2021/03/03/sam-bam/ https://samtools.github.io/hts-specs/SAMv1.pdf SAM - Sequence Alignment Map BAM - Binary Alignment Map
45. GENOME EXOME gene
46. Variant calling https://en.wikipedia.org/wiki/SNV_calling_from_NGS_data
47. https://en.wikipedia.org/wiki/Variant_Call_Format VCF – Variant Call Format, BCF – Binary Call Format https://samtools.github.io/hts-specs/
49. A – C – A – G – G – A – T – G – A – T – A – A – C – G – G – G – T – T – C - A patient
50. A – C – T – G – G – A – T – C – A – T – A – C – C – G – G – G – A – T – C - A A – C – A – G – G – A – T – G – A – T – A – A – C – G – G – G – T – T – C - A patient reference
51. A – C – T – G – G – A – T – C – A – T – A – C – C – G – G – G – A – T – C - A A – C – A – G – G – A – T – G – A – T – A – A – C – G – G – G – T – T – C - A patient reference A – C – A – G – G – A – T – C – A – T – A – C – C – G – G – G – A – T – C - A A – C – T – G – G – A – T – C – A – T – A – C – C – G – G – G – T – T – C – A A – C – A – G – G – A – T – C – A – T – A – C – C – C – G – G – A – T – C – A A – C – T – G – A – A – T – G – A – T – A – C – C – G – G – G – A – T – C – A A – C – T – G – G – A – T – C – A – T – A – C – C – G – G – G – A – T – C – A A – C – T – G – A – A – T – G – A – T – A – C – C – G – G – G – A – T – C – T A – C – A – G – G – A – T – C – A – T – A – C – C – C – G – G – A – T – C – A A – C – T – G – G – A – T – C – A – T – A – C – C – G – G – G – A – T – C – A population
52. Making sense of the variants • Variant Filtering – Removes low-quality or irrelevant variants to focus on high-confidence data. • Variant Annotation – Adds biological context and functional predictions to each variant. • Variant Prioritization – Ranks variants based on predicted impact, known associations, and relevance to the study. • Variant Interpretation – Assesses the biological and clinical significance of prioritized variants using functional predictions, clinical databases, and literature review. Population specific databases
53. How long did it take?  HPC, Cloud Analysis Type Initial Processing (CPU hours) Alignment and Variant Calling (CPU hours) Secondary Analysis (CPU hours) Total (CPU hours) Whole Genome Sequencing (WGS) 50-100 100-200 50-100 200-400 Whole Exome Sequencing (WES) 20-40 50-100 20-40 90-180 Targeted Sequencing 5-10 10-20 5-10 20-40
54. Bioinformatics Workflow FASTQ SAM/ BAM VCF annotated VCF mapping variant calling variant annotation variant filtering variant prioritization variant interpretation candidate variant/s
55. Sanger sequencing Segregation Recurrence In-vitro and in-vivo models Variant validation
56. Variant confirmed as disease causing! What next? • Existing treatment  Yupii!! • Some drug that affects the same metabolical patway?  Repurpose • No treatment – Help the family • Genetic counselling • Family planning (IVF) – Spread the world • Publish • Submit to databases • Connect with other researchers – Start working on one
57. Variant not identified? What now? • Was it well sequenced? – NO  Sequenced again – YES  Check for other types of variants • Reanalyze with different tools, settings, reference, annotations • Wait and ranalyze later with updated annotations • Used Illumina?  Try 3rd generation sequencer • Maybe is not genetic?
58. NGS sequencing improved clinical diagnostics from 1% up to 50%
59. COOL, BUT WHERE IS PYTHON?
60. Bioinformatics Workflow FASTQ SAM/ BAM VCF annotated VCF mapping BWA Bowtie2 Novoalign variant calling GATK FreeBayes VarScan SAMtools variant annotation ANNOVAR SnpEff VEP variant filtering VCFtools Gemini variant prioritization SIFT PolyPhen CADD Exomiser variant interpretation ClinVar OMIM HGMD
61. Bioconda • Lets you install thousands of software packages related to biomedical research using the conda package manager. https://bioconda.github.io/
62. Workflow management system • Snakemake – Python • Nextflow – Polyglot • Open WDL – A community driven data processing language
63. A community effort to collect a curated set of analysis pipelines built using Nextflow.
64. Bioinformatics Workflow FASTQ SAM/ BAM VCF annotated VCF mapping BWA Bowtie2 Novoalign variant calling GATK FreeBayes VarScan SAMtools variant annotation ANNOVAR SnpEff VEP variant filtering VCFtools Gemini variant prioritization SIFT PolyPhen CADD Exomiser variant interpretation ClinVar OMIM HGMD
65. Biopython Python Tools for Computational Molecular Biology • Wiki documentation Seq and SeqRecord objects • Bio.SeqIO - sequence input/output • Bio.AlignIO - alignment input/output • Bio.PopGen - population genetics • Bio.PDB - structural bioinformatics • Biopython’s BioSQL interface
66. Biopyton from Bio.Seq import Seq # Define a DNA sequence dna_sequence = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG") print("DNA Sequence:", dna_sequence) # Transcribe (T -> U + reverse complement) reverse_complement_dna_sequence = dna_sequence.reverse_complement() rna_sequence = reverse_complement_dna_sequence.transcribe() # Print the RNA sequence print("RNA Sequence:", rna_sequence) # Translate the RNA sequence to a protein sequence protein_sequence = rna_sequence.translate() # Print the protein sequence print("Protein Sequence:", protein_sequence) DNA Sequence: ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG RNA Sequence: CUAUCGGGCACCCUUUCAGCGGCCCAUUACAAUGGCCAU Protein Sequence: LSGTLSAAHYNGH
67. Bioinformatics Workflow FASTQ SAM/ BAM VCF annotated VCF mapping BWA Bowtie2 Novoalign variant calling GATK FreeBayes VarScan SAMtools variant annotation ANNOVAR SnpEff VEP variant filtering VCFtools Gemini variant prioritization SIFT PolyPhen CADD Exomiser variant interpretation ClinVar OMIM HGMD
68. CASE STUDY Genetic disease
69. CASE STUDY Patient Profile: • Demographics: Healthy Caucasian female • Reproductive History: Experienced 3 miscarriages at 8-10 weeks (Recurrent Pregnancy Loss). Medical Investigations: • Comprehensive Examinations: gynecologycal examination, genetics, reproductive immunology, general immunology, hysteroscopy, myology, hematology, physiotherapy • Immunological Findings: Detected meningococcal and streptococcal infections in sputum, treated successfully with antibiotics. • Other findings: healthy
70. Whole genome sequencing Genetic Findings: • Gene: MTHFR (Methylenetetrahydrofolate Reductase) • Mutation: Homozygous C677T Clinical Significance: • The C677T mutation in the MTHFR gene results in a substitution of cytosine (C) with thymine (T) at position 677. • Homozygous individuals (TT) for this mutation have significantly reduced MTHFR enzyme activity (30%-35% of normal activity).
71. MTHFR, rs1801133 Frequency of the allele in European (non-Finnish) - 34% (https://gnomad.broadinstitute.org/)
72. MTHFR https://pubmed.ncbi.nlm.nih.gov/32827402/
73. Literature search 100 mg/day aspirin 5 mg/day folic acid 100 mg/day aspirin 5 mg/day folic acid 0.4 mg/day enoxaparin triparous women without RM or thrombophilia Group 1 Group 2 Control group 123 123 117 46.3% 79.7% 86.3% Delivery rate Patients Conclusion: Treatment with low-dose aspirin, enoxaparin and folic acid was the most effective therapy in women with RM who carried a C677T MTHFR mutation.
74. Treatment Treatment Approach: • Protocol: The patient underwent a treatment regimen described in a study comparing preventive treatments for patients with recurrent miscarriages carrying the C677T MTHFR mutation. • Modification: Instead of folic acid, the patient used active folic acid (5-methyl THF). Outcome: • Following the treatment with aspirin, 5-methyl THF and enoxoparin, the patient successfully conceived and delivered a healthy baby.
75. It takes 15 – 17 years from research to clinic
76. Actively pursuing self-diagnosis can be successful
77. Formal and informal networks
78. Thank you for your attention! Questions? https://www.linkedin.com/in/pristanna/