Post on 17-Feb-2019
Lezioni di genetica medica
2017
Testi consigliati
• Neri-GenuardiGenetica umana e medicaEditore Elsevier Masson
• Moncharmont-LeonardiPatologia GeneraleEditore Idelson Gnocchi
• Strachan-ReadGenetica Molecolare UmanaEditore Zanichelli
• LewisGenetica UmanaEditore Piccin
• Sito web http://www.vincenzonigro.it (glossario)
Renato Dulbecco premio Nobel per la medicina 1975Catanzaro, 22 febbraio 1914 – La Jolla, 19 febbraio 2012
Science 7 marzo 1986: «leggere tutto il genoma umano»
• Costo 3 miliardi di dollari
• 13 anni (1988-2001)
Progetto genomaumano
1 245,203,898 218,712,898
2 243,315,028 237,043,673
3 199,411,731 193,607,218
4 191,610,523 186,580,523
5 180,967,295 177,524,972
6 170,740,541 166,880,540
7 158,431,299 154,546,299
8 145,908,738 141,694,337
9 134,505,819 115,187,714
10 135,480,874 130,710,865
11 134,978,784 130,709,420
12 133,464,434 129,328,332
13 114,151,656 95,511,656
14 105,311,216 87,191,216
15 100,114,055 81,117,055
16 89,995,999 79,890,791
17 81,691,216 77,480,855
18 77,753,510 74,534,531
19 63,790,860 55,780,860
20 63,644,868 59,424,990
21 46,976,537 33,924,742
22 49,476,972 34,352,051
X 152,634,166 147,686,664
Y 50,961,097 22,761,097
Lunghezza dei cromosomi totale eucromatina
UCSC Genome Browser
Screenshot from University of California at Santa Cruz http://genome.ucsc.edu
storia:Sanger sequencing
Frederick SangerNobel price 1958 and 1980born August 13 1918, died November 19 2013
I ddNTPs (ddATP, ddTTP, ddGTP, ddCTP) sono detti terminatori perché bloccano la polimerizzazione del DNA
“prima generazione”sequenziamento del DNA secondo il metodo Sanger
Il sequenziamento con tutte le versioni modificate del metodo Sanger ha dominato nella scienza e nell’industria per almeno 20 anni e ha consentito la lettura del genoma umano e la scoperta di oltre 2.500 malattie genetiche monogeniche
Il metodo Sanger rappresenta la tecnologia di “prima generazione”, mentre i nuovi metodi sono denominati “next-generation sequencing (NGS)”
Metodo “Sanger”
• prima si isolano segmenti di DNA chimicamente omogenei
• si replicano fino a nanogrammi di DNA
• poi partono le singole reazioni
Mutation detection by Sanger sequencinggene-by-gene, exon-by-exon, fragment-by-fragment
2a generazione“next-generation sequencing (NGS)”
• Il principale vantaggio è poter lavorare con milioni di molecole di DNA
senza doverle separare
• la possibilità tecnica di produrre un volume enorme di dati a costi
estremamente più bassi ed in tempi estremamente più rapidi
• Il potenziale dell’NGS è simile ai primi tempi della PCR con il limite
principale dovuto all’immaginazione
2a generazione“next-generation sequencing (NGS)”
• prima si frammenta il DNA casualmente
• si aggiungono adattatori comuni
• poi partono milioni di reazioni di sequenziamento individuali
Sistema Illumina
• ciascun frammento di DNA è immobilizzato su una superficie solida
• molecole di DNA spazialmente separate permettono di eseguire
simultaneamente milioni di reazioni di sequenziamento
• ciascuna reazione produce una fluorescenza puntiforme che è fotografata
• la scansione di queste immagini consente di leggere la sequenza per ogni
punto
la Solid-phase amplification può generare milioni di clusters di
molecole di DNA distinte per vetrino
flow cell
Bridge PCR
Genome Analyzer II
4.000.000.000 bp/7 giorni
= 42 ore per miliardo di basi
350$/miliardo di basi
2008
NovaSeq6000
6.000.000.000.000 bp/40 ore
= 40 secondi per miliardo di basi
7$/miliardo di basi
20172002
96-capillary 3730xl DNA Analyzer
2.100.000 bp/giorno
= 476 giorni per miliardo di basi
2.000.000$/miliardobasi
• Next generation sequencing = 25,594 papers
– Before 2010 : 221
• Exome = 8,341 papers
– Before 2010 : 8
http://www.ncbi.nlm.nih.gov/pubmedOctober 26, 2016
FASTQ
@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATA
+
!''*((((***+))%%%++)(%%%%).1***1
ACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGAACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGATAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGATAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTT
Variazioni di sequenza in un segmento di DNA
Ogni cromosoma è differente da tutti gli altri
SNPssingle nucleotide polymorphisms
• Variazioni naturali che esistono tra le sequenze di qualsiasi cromosoma con un frequenza di almeno l’1%degli individui
• Consistono in sostituzioni di singoli nucleotidi, altre più rare consistono in delezioni o inserzioni di singoli nucleotidi
• Un SNP è identificato mediante sequenziamento del DNA di differenti cromosomi in individui diversi
• I due alleli possono essere identici (in omozigosi; T/T o C/C) o differenti (eterozigosi T/C o C/T) nel sitopolimorfico
penetrance
the penetrance of a disease-causing variant is
the proportion of individuals with the pathogenic gene variant who exhibit clinical symptoms
for example, if a gene variant transmitted as an autosomal dominant trait has 75% penetrance, then 75% of those with the variant will develop the disease, while 25% will not
expressivity
the expressivity of a disease-causing variant is
a variation in a phenotype among individuals carrying the same gene variant(s)
for example, in cystic fibrosis the range extends from early childhood death due to obstructive lung disease with bronchiectasis, to pancreatic insufficiency to recurrent sinusitis and bronchitis or male infertility in young adulthood
It arises when a person has a large amount of data at disposal, but only focuses on a small subset of that data
the Texas Sharpshooter Fallacy
Variant interpretation
• Population data EXac, EVS, 1,000 genomes• Internal database (local population, NIG?)• Segregation data• Functional studies and predictive tools• Impact of other variants on the same gene (Stop?)• Relationship with the observed phenotype
• very strong (PVS1)• strong (PS1–4)
• moderate (PM1–6)• supporting (PP1–5)
Pathogenic
likely pathogenic
uncertain significance
likely benign and benign
4,200,000/WGS 71,000/WES
sequence variants
interpretation
The genome of James Watson
7.4 x coverage
234 runs
24.5 billions bp
11 mutazioni causative di malattie genetiche
“Whole - Genome” Seq
Pedigree of the family and segregation of SH3TC2 mutations
Sequenze codificanti dei geni umani
Geni con mutazioni che causanomalattie umane
45M = ~ 21.000 geni
45 M >4.500 geni
Totale ~ 3.100 M
le mutazioni che causano le malattie monogeniche (mendeliane) sono concentrate nello 0,3% del genoma
1
5
50
500
Mendeliome: 5,000
whole exome: 20,000
whole genome
How much workload?How much money?
Il termine “NGS” indicatipologie di analisi molto differenti
Whole genomecoverage @40 x (~ 150 Gb of sequences/sample) = 1,000 Gbytes
Whole exomecoverage @60 x (~ 8 Gb/sample)= 60 Gbytes/sample
Solo 10-15 geni noticoverage @200 x (~ 0.1 Gb/sample)= 1 Gbytes/sample
• whole genome (WGS)
• whole exome (WES)
• broad targeted sequencing
• focused targeted sequencing
• specific variants (deep sequencing) Whole genome
@30 x(~ 150 Gb)
Whole exome
@100 x(~ 10 Gb)
Targeted
@500 x(~ 0.1-1
Gb)
“Next Generation Sequencing”poor results?
30-40% 30-40% >40%
detection rate
NGS parents should be included to
• validate variants
• ruling out sample swaps
• discover de novo mutations and get phase
But run separately and examined together……..
The accuracy of NGS is improving
New kits improve coverage uniformity in Mendeliome, WES, WGS
New chemistry improve sequence signal
New instruments (NovaSeq) improve read length
More powerful bioinformatic softwares
Public databases are much larger
Progress of WES and WGS quality (from GWE Santen)
22/11/2017
39
2011 exome 30x
2011 genome 25X
2014 exome 160x
2014 exome 60x
2015 genome (X10)
Whole exome con CRE Agilent (sequenze almeno 20x) in 84 soggetti, di cui 1/3 fatti con doppio coverage (20Gb)
COL6A1 100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
99.7 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100
COL6A2 100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 99.6 99.6 100 100 100 99.9 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 99.6 100
COL6A3 100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100 100 100 100 100 100 100
100 100 100
may be a new genetic disease? case submission
evaluation and deep phenotyping by the
three clinical partners
Experimental plan
Case 3
Case 1 Cas
e 2
selection and prioritization of 350
families
high-coverage whole exome sequencing and
validation
DNA samples from father-mother-
propositus
matching DNA variants with those of similar
phenotypesthroughout the world
return of results and counselling
The Starry Night, 1889
Vincent van Gogh
recognize the picture?
no idea
one crucial de novo mutation
absent absent
Come scoprire un’eventuale nuova malattia genetica da mutazioni denovo usando l’NGS?
l’idea di base
Data will be mainly shared through
PhenomeCentral (http://phenomecentral.org)
This has great potential for identifying
additional cases of phenotypically similar
patients required to validate the identified
putative disease causative variant(s)
>350 users worldwide, such as the
Canadian (Care for Rare), US
(Undiagnosed Diseases Network)
and European (Neuromics, AnDDI-
rare) rare disease sequencing
programs
Matching Sequence and Phenotypic Data
two mutations in the same gene (1/360,000)
1/300 1/300
heterozygous healthy carriers, rare patients
Come scoprire una nuova malattia genetica a trasmissione autosomica recessiva usando l’NGS?
Malattie da mutazioni in 2 alleli
patologie a trasmissione autosomica recessiva
in genere «loss-of-function»
le mutazioni nuove sono rare, di solito hanno una lunga storia (100-1000 generazioni) con un fondatore
hanno una «ethnical signature» con un pattern di distribuzione geografica riconoscibile
la consanguineità è pertanto il fattore principale di rischio
è più facile distinguere i polimorfismi, ma molti portatori
con la penetranza al 50% e la frequenza di 1/20.000, gli alleli causativi possono avere nella popolazione generale una frequenza fino a 0.02
accuracytechnique/variant class
Variant type Sanger Array CGH Targeted NGS WGS PCR-free
substitution +++ - +++ +++
indel +++ - + +
triplet + - - +
Exon del/dup - + - +
Large del/dup - +++ -/+ ++
Quali varianti del DNA possono essere perse dall’NGS?
Non identificate• quelle in regioni con
poca/assente copertura
• inserzioni/delezioni >10-20nt
• Espansioni di triplette
• copy number variations
• traslocazioni
• inversioni
Non riconosciute• Difetti di splicing elusivi
• Varianti in geni a funzione sconosciuta
• Varianti multiple di geni molto grandi
ACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGAACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGATAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGATAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGA
ACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGAACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACTATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGATAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGA
1
2
Copy Number VariationCNV
trovare un’informazione importante, senza sapere dove sia
but you have paper strips to be read..
long range information, phasing, structural variant detectionand copy number determination