Results

Here are the results for simulated and real data.

Note

retroCNVs - polymorphic retrocopies

Simulated data

Our dataset for testing is composed of 100 simulated human whole-genome sequencing with 20x of depth and in average 30 randomly distributed retrocopies each. Simulation with low coverage of (‘only’) 20x in sequencing depth (i.e., heterozygotic events have only 10x coverage). This strategy allowed us to check the capability of sideRETRO to identify retroCNVs events even in a “non-ideal scenario” of low sequencing coverage. In total, we had a list of 100 retrocopies consisting of the last 1000 bases of the largest transcript of the parental gene - which were randomly raflled as well. All retrocopies was stochastically designed for chromosome, position, strand and zygosity.

The simulated retrocopy data is composed of three sets of retroCNVs events:

  1. fixed or highly frequent events;
  2. polymorphic events (shared by some of the simulated genomes);

iii) somatic events (in only one genome) in simulation. It allowed us to check sideRETRO performance for these different types of retroCNVs.

Simulation

We developed a pipeline, which randomly generates our simulated dataset and make some analysis of performance. All scripts can be downloaded at simulation.tar.gz. We used the SANDY tool (version v0.23), A straightforward and complete next-generation sequencing read simulator [2], for simulate all 100 genomes according to the structural variations that we designed and according to the sampling. We used the reference human genome v38 and the GENCODE annotation v32.

REF_FASTA=/assets/hg38.fa
PC_FASTA=/assets/gencode.v32.pc_transcripts.fa
COHORT=100
RTC_NUM=100
LEN=1000
DEPTH=20
SANDY_SEED=1
SEED=17

# Genearte sequences
scripts/catch \
  --seed=$SEED \
  --rtc_num=$RTC_NUM \
  --length=$LEN \
  "$PC_FASTA" > rtc_100.tsv

# Build our cohort
scripts/build \
  --cohort=$COHORT \
  --seed=$SEED \
  --output-dir=build \
  "$REF_FASTA" \
  rtc_100.tsv

# Retrocopies by individual
IND=($(ls build/*.sandy))

# Load build values to SANDY
for ind in "${IND[@]}"; do
  sandy variation add \
    --structural-variation=$(basename $ind '.sandy') \
    $ind
done

mkdir -p sim

# Simulate all genomes
for ind in "${IND[@]}"; do
  sandy_index=$(basename $ind '.sandy')
  sandy genome \
    --id='%i.%U_%c:%S-%E_%v' \
    --structural-variation=$sandy_index \
    --output-dir="sim/$sandy_index" \
    --jobs=20 \
    --seed=$SANDY_SEED \
    --quality-profile='hiseq_101' \
    --coverage=$DEPTH \
    --verbose \
    $REF_FASTA
done

As result we have a pair of FASTQ files (forward and reverse complement) for each simulated individual. Next it is required to align our sequencing data against the human reference genome in order to generate mapped files in SAM format. We used BWA aligner (version 0.7.9) [3] for accomplish this task.

# Individual directories with the
# simulated data
IND_DIR=($(ls -d sim/*))

# Reference genome
REF_FASTA="/assets/hg38.fa"

# Index reference genome
bwa index $REF_FASTA

mkdir -p align

# Alignment
for ind in "${IND[@]}"; do
  id="$(basename $ind)"
  bwa mem \
    -t 10 \
    $REF_FASTA \
    $ind/out_R1_001.fastq.gz \
    $ind/out_R2_001.fastq.gz > "align/$id.sam"
done

After our simulated dataset was ready, we run sideRETRO v0.14.1:

# Our simulated SAM files list
LIST=($(ls align/*.sam))

# GENCODE annotation v32
ANNOTATION=/assets/gencode.v32.annotation.gff3

# GENCODE reference genome
REF_FASTA=/assets/hg38.fa

# Run process-sample step
sider process-sample \
  --prefix=sim \
  --cache-size=20000000 \
  --output-dir=sider \
  --threads=20 \
  --alignment-frac=0.9 \
  --phred-quality=20 \
  --sorted \
  --log-file=ps.log \
  --annotation-file=$ANNOTATION \
  "${LIST[@]}"

# Run merge-call step
sider merge-call \
  --cache-size=20000000 \
  --epsilon=500 \
  --min-pts=10 \
  --log-file=mc.log \
  --threads=20 \
  --phred-quality=20 \
  --in-place \
  sider/sim.db

# Finally run make-vcf
sider make-vcf \
  --log-file=vcf.log \
  --reference-file=$REF_FASTA \
  --prefix=sim \
  --output-dir=sider \
  sider/sim.db

Finally, with the sideRETRO’s VCF made, we analysed the performance:

# Generate comparations for analysis
scripts/compare sider/sim.vcf build

# Confusion analysis
scripts/confusion analysis > confusion.tsv

# Just a look
$ column -t confusion.tsv | head
IND                TP  FP  FN   PPV/Precision  TPR/Recall  F1-score
analysis/ind0.tsv  38  0   9    1.000000       0.808511    0.894118
analysis/ind1.tsv  36  2   11   0.947368       0.765957    0.847059
analysis/ind2.tsv  33  1   10   0.970588       0.767442    0.857143
analysis/ind3.tsv  35  1   12   0.972222       0.744681    0.843373
analysis/ind4.tsv  29  1   9    0.966667       0.763158    0.852941
analysis/ind5.tsv  37  4   12   0.902439       0.755102    0.822222
analysis/ind6.tsv  45  0   10   1.000000       0.818182    0.900000
analysis/ind7.tsv  37  2   11   0.948718       0.770833    0.850575
analysis/ind8.tsv  32  2   11   0.941176       0.744186    0.831169

Analysis

Summary of the set of 100 simulated retroCNVs. Simulated retroCNV events were randomly inserted in the human genome (GRCh38). Here, we present their parental gene name, the insertion point, polarity (Pol). All events found (79 retroCNVs) and not found (21 retroCNVs) are presented, as well as addition information about their insertion point (considering a region of 100bp around its position)
Parental Gene SIMULATED FOUND (79 events)
Chr Position Pol LINE/SINE Chr Position Pol
ALG2 chr10 30778982 - N chr10 30778981 -
ARMC2 chr5 52723637 - Y chr5 52723638 -
ATG2B chr5 177026995 - N chr5 177026990 -
BTF3 chr7 146774631 - N chr7 146774629 -
C2orf92 chr6 112158328 - N chr6 112158327 -
C8orf76 chr9 94927085 - N chr9 94927084 -
C9orf64 chr17 40139106 + Y chr17 40139104 +
CABP7 chr5 153788597 + Y chr5 153788596 +
CARD8 chrX 99922659 + N chrX 99922658 +
CASTOR3 chr3 189081695 - N chr3 189081692 -
CDH22 chr9 113306486 - Y chr9 113306485 -
CFAP69 chr11 10733916 - N chr11 10733915 -
COL4A3 chr16 46427444 + N chr16 46427444 +
COPS2 chr1 38773310 - Y chr1 38773309 -
CPNE7 chr9 42228417 + Y chr9 42228469 .
DENND2D chr18 37314709 + N chr18 37314708 +
DNAJC27 chr12 60940050 - N chr12 60940049 -
EPC2 chr13 94468157 - N chr13 94468156 -
EPS8 chr21 26428011 + N chr21 26428011 +
ERCC4 chr6 93262920 + N chr6 93262919 +
FAAP20 chr9 77384901 - N chr9 77384898 -
FAM177B chr12 130498191 + N chr12 130498188 +
FAM71E2 chr2 225319689 + N chr2 225319688 +
HAO2 chr14 69901152 + N chr14 69901150 +
HEG1 chr3 15517386 - Y chr3 15517382 -
HIP1 chr8 75177754 + Y chr8 75177754 +
IL1R1 chr8 30386429 - N chr8 30386427 -
IQGAP3 chr6 124358143 + Y chr6 124358101 +
KIF7 chrX 89251626 - Y chrX 89251603 -
LAMP1 chr13 87908197 - N chr13 87908197 -
LARS chr9 64069435 + Y chr9 64069377 +
LRRC6 chr4 180728002 - N chr4 180728002 -
MACROD2 chr20 18178487 + N chr20 18178486 +
MYH10 chr4 186290075 + Y chr4 186290074 +
MYH7B chr13 104241206 + N chr13 104241205 +
MYO7A chr11 14072547 + N chr11 14072546 +
NAE1 chr18 74528384 + Y chr18 74528383 +
OR14A16 chr1 52758590 + N chr1 52758589 +
OR51M1 chr2 37409208 - N chr2 37409207 -
OSER1 chr5 53846631 - Y chr5 53846596 -
PAFAH1B1 chr15 86208543 + Y chr15 86208562 +
PDGFB chr8 133462380 - N chr8 133462379 -
PFKFB2 chr5 36822019 - N chr5 36822019 -
PLCB1 chr9 25165703 + Y chr9 25165702 +
PNRC1 chr15 48607415 + N chr15 48607414 +
PRMT2 chr8 50511539 - Y chr8 50511540 -
PRPF18 chr20 51460729 + Y chr20 51460728 +
PRSS45P chr19 5420707 - Y chr19 5420706 -
PTPRF chr19 7227546 + Y chr19 7227546 +
RAB18 chr4 10281361 - N chr4 10281361 -
RAB5B chr6 46561322 + N chr6 46561322 +
RADX chr12 117277769 + N chr12 117277768 +
RASGEF1C chr5 115992817 + N chr5 115992816 +
RBM4 chr7 101199285 + Y chr7 101199284 +
RMDN3 chr3 28655572 - N chr3 28655571 -
RNF6 chr4 39797761 - Y chr4 39797759 -
SART1 chr2 109317943 + N chr2 109317942 +
SDHA chr4 179658356 + N chr4 179658355 +
SEZ6L chr18 560651 - Y chr18 560650 -
SKP2 chr5 88746051 - N chr5 88746050 -
SLC9A3 chr4 140369141 - N chr4 140369139 -
SMTNL2 chr3 144112843 - N chr3 144112842 -
SNRNP27 chrX 13251389 - N chrX 13251387 -
STK17B chrX 36995058 - Y chrX 36995057 -
TACO1 chrY 12987416 + Y chrY 12987415 +
TMEM63C chr17 49131966 + Y chr17 49131965 +
TMEM95 chr2 234301985 - Y chr2 234301984 -
TSFM chr12 80384739 - Y chr12 80384736 -
TUBGCP2 chr1 197233691 + N chr1 197233690 +
VIPAS39 chr12 54021508 - N chr12 54021507 -
WDR74 chr11 112552782 - N chr11 112552781 -
WDR75 chr6 132636317 + Y chr6 132636316 +
ZNF136 chr16 59509103 + Y chr16 59509104 +
ZNF326 chr8 29273486 - Y chr8 29273482 -
ZNF385A chr12 92752469 - N chr12 92752468 -
ZNF431 chr16 88101015 - N chr16 88101015 -
ZNF585A chr18 78888223 - Y chr18 78888222 -
ZNF738 chr6 139608184 - N chr6 139608183 -
ZNF793 chr9 120420222 + N chr9 120420223 +
RetroCNV events not found by sideRetro (21 events)
  Duplicated region
AC002310.4 chr9 94545202 - N chr8:115819078-115819180
AC135178.3 chr7 74794901 - N chr7:75151009-75151108
ACSBG2 chr21 43058887 - N chr21:6450515-6450614
ADD2 chr3 9759497 + N No
AL645922.1 chr6 38626680 - N No
C21orf91 chr14 54886570 - Y Duplications: 7x genome
CERS1 chr20 41341204 + N No
CWC25 chr13 39475646 - N No
DHRSX chr5 166496220 - Y Highly repetitive region
LETM1 chrY 24793930 - N 8 identical region in chrY
MALL chr7 110598366 + N No
MRPS7 chr2 1490696 + N chr2_KI270774v1_alt
MTNR1A chr8 86938090 - N chrX, chr4
NDUFA6 chr10 38060463 + N chr10:42588649-42588750
PLAC8 chr9 39225441 + Y chr9:61393599-61393698
PTCHD4 chr15 31035142 - Y chr15_KI270905v1_alt
SLC44A4 chrY 4417954 + Y chrX:90835484-90835583
STON2 chrX 468106 + N chrY:468056-468155
TAF7 chr22 22384919 - N chr22_KI270875v1_alt
TBC1D3F chr16 65760883 + Y No
TRIM40 chr5 45713519 + N No
sideRETRO capability to identify simulated retroCNVs common (present in all simulated genomes), polymorphic (events present in > 2 genmes) and somatic (events present in only an individual genome).
RetroCNV type # of simulated events Found events %
Common 25 19 76
Polymorphic 50 42 84
Somatic 25 18 72
sideRetro performance in identifying simulated retroCNVs. It is shown gene genome coverage, the true positive, false negative, false positive, precision, recall and F1-score considering all simulated retroCNVs (*) and also using those 86 events (**) inserted in mappeable (non ambiguous) genomic regions. These scores are given to the full set of 100 simulated genomes.
Ind TP FP FN* PPV TPR (|*) F1 (|*)
0 38 0 9|5 1.00 0.81|0.88 0.89|0.94
1 36 2 11|7 0.95 0.77|0.84 0.85|0.89
2 33 1 10|6 0.97 0.77|0.85 0.86|0.90
3 35 1 12|5 0.97 0.74|0.88 0.84|0.92
4 29 1 9|5 0.97 0.76|0.85 0.85|0.91
5 37 4 12|5 0.90 0.76|0.88 0.82|0.89
6 45 0 10|6 1.00 0.82|0.88 0.90|0.94
7 37 2 11|5 0.95 0.77|0.88 0.85|0.91
8 32 2 11|5 0.94 0.74|0.86 0.83|0.90
9 33 3 11|5 0.92 0.75|0.87 0.83|0.89
10 34 1 9|5 0.97 0.79|0.87 0.87|0.92
11 37 2 12|5 0.95 0.76|0.88 0.84|0.91
12 30 1 10|5 0.97 0.75|0.86 0.85|0.91
13 43 3 11|5 0.93 0.80|0.90 0.86|0.91
14 38 0 10|6 1.00 0.79|0.86 0.88|0.93
15 31 1 8|5 0.97 0.79|0.86 0.87|0.91
16 30 4 13|6 0.88 0.70|0.83 0.78|0.86
17 39 1 9|5 0.98 0.81|0.89 0.89|0.93
18 37 0 10|5 1.00 0.79|0.88 0.88|0.94
19 39 1 10|6 0.98 0.80|0.87 0.88|0.92
20 39 2 12|6 0.95 0.76|0.87 0.85|0.91
21 42 3 12|5 0.93 0.78|0.89 0.85|0.91
22 39 0 10|6 1.00 0.80|0.87 0.89|0.93
23 41 2 10|5 0.95 0.80|0.89 0.87|0.92
24 43 1 8|5 0.98 0.84|0.90 0.91|0.93
25 41 0 9|6 1.00 0.82|0.87 0.90|0.93
26 43 0 10|6 1.00 0.81|0.88 0.90|0.93
27 34 0 10|5 1.00 0.77|0.87 0.87|0.93
28 38 4 14|7 0.90 0.73|0.84 0.81|0.87
29 36 1 11|6 0.97 0.77|0.86 0.86|0.91
30 47 3 11|5 0.94 0.81|0.90 0.87|0.92
31 43 3 12|5 0.93 0.78|0.90 0.85|0.91
32 38 0 11|5 1.00 0.78|0.88 0.87|0.94
33 34 1 12|6 0.97 0.74|0.85 0.84|0.91
34 35 4 12|6 0.90 0.74|0.85 0.81|0.88
35 43 2 10|6 0.96 0.81|0.88 0.88|0.91
36 41 2 11|6 0.95 0.79|0.87 0.86|0.91
37 38 1 11|6 0.97 0.78|0.86 0.86|0.92
38 34 1 9|5 0.97 0.79|0.87 0.87|0.92
39 39 0 8|5 1.00 0.83|0.89 0.91|0.94
40 35 1 9|5 0.97 0.80|0.88 0.88|0.92
41 33 1 9|5 0.97 0.79|0.87 0.87|0.92
42 39 1 11|7 0.98 0.78|0.85 0.87|0.91
43 37 4 13|7 0.90 0.74|0.84 0.81|0.87
44 39 4 13|6 0.91 0.75|0.87 0.82|0.89
45 35 3 11|6 0.92 0.76|0.85 0.83|0.89
46 31 0 9|5 1.00 0.78|0.86 0.87|0.93
47 36 0 10|5 1.00 0.78|0.88 0.88|0.94
48 40 3 11|6 0.93 0.78|0.87 0.85|0.90
49 34 1 10|5 0.97 0.77|0.87 0.86|0.92
50 41 4 13|6 0.91 0.76|0.87 0.83|0.89
51 34 0 9|5 1.00 0.79|0.87 0.88|0.93
52 36 3 12|5 0.92 0.75|0.88 0.83|0.90
53 39 2 11|5 0.95 0.78|0.89 0.86|0.92
54 47 0 10|6 1.00 0.82|0.89 0.90|0.94
55 36 1 12|5 0.97 0.75|0.88 0.85|0.92
56 40 2 12|6 0.95 0.77|0.87 0.85|0.91
57 41 1 9|5 0.98 0.82|0.89 0.89|0.93
58 40 0 10|5 1.00 0.80|0.89 0.89|0.94
59 34 3 11|6 0.92 0.76|0.85 0.83|0.88
60 35 2 10|5 0.95 0.78|0.88 0.85|0.91
61 38 1 9|5 0.97 0.81|0.88 0.88|0.93
62 30 1 8|5 0.97 0.79|0.86 0.87|0.91
63 38 4 13|6 0.90 0.75|0.86 0.82|0.88
64 43 2 10|5 0.96 0.81|0.90 0.88|0.92
65 46 1 10|6 0.98 0.82|0.88 0.89|0.93
66 41 1 10|6 0.98 0.80|0.87 0.88|0.92
67 37 2 9|5 0.95 0.80|0.88 0.87|0.91
68 44 5 13|6 0.90 0.77|0.88 0.83|0.89
69 36 0 9|5 1.00 0.80|0.88 0.89|0.94
70 42 4 14|7 0.91 0.75|0.86 0.82|0.88
71 44 3 14|7 0.94 0.76|0.86 0.84|0.90
72 41 3 13|6 0.93 0.76|0.87 0.84|0.90
73 34 1 9|5 0.97 0.79|0.87 0.87|0.92
74 42 1 10|5 0.98 0.81|0.89 0.88|0.93
75 37 3 11|5 0.93 0.77|0.88 0.84|0.90
76 34 2 9|5 0.94 0.79|0.87 0.86|0.91
77 37 3 10|5 0.93 0.79|0.88 0.85|0.90
78 38 0 8|5 1.00 0.83|0.88 0.90|0.94
79 40 2 9|5 0.95 0.82|0.89 0.88|0.92
80 35 0 9|5 1.00 0.80|0.88 0.89|0.93
81 40 1 10|6 0.98 0.80|0.87 0.88|0.92
82 41 2 11|7 0.95 0.79|0.85 0.86|0.90
83 39 2 11|6 0.95 0.78|0.87 0.86|0.91
84 40 3 10|6 0.93 0.80|0.87 0.86|0.90
85 36 4 12|5 0.90 0.75|0.88 0.82|0.89
86 37 4 13|6 0.90 0.74|0.86 0.81|0.88
87 32 2 11|5 0.94 0.74|0.86 0.83|0.90
88 42 2 12|7 0.95 0.78|0.86 0.86|0.90
89 34 1 9|5 0.97 0.79|0.87 0.87|0.92
90 41 2 10|5 0.95 0.80|0.89 0.87|0.92
91 45 0 9|6 1.00 0.83|0.88 0.91|0.94
92 39 2 8|5 0.95 0.83|0.89 0.89|0.92
93 39 2 11|6 0.95 0.78|0.87 0.86|0.91
94 34 3 12|5 0.92 0.74|0.87 0.82|0.89
95 44 4 11|5 0.92 0.80|0.90 0.85|0.91
96 36 1 9|5 0.97 0.80|0.88 0.88|0.92
97 39 2 10|5 0.95 0.80|0.89 0.87|0.92
98 48 0 9|6 1.00 0.84|0.89 0.91|0.94
99 40 0 10|6 1.00 0.80|0.87 0.89|0.93
Total 3806 172 1051|551 0.96 0.78|0.87 0.86|0.91
_images/result_confusion.png

Overall performance for 86 simulated retroCNV events in mappeable genomic regions (Imbalanced confusion matrix). True Positive (TP), False Negative (FN), False Positive (FP), True Positive Rate or Recall (TPR), Positive Predictive Value or Precision (PPV) and F1 score.

Real data

The method developed and used by Abyzov et al. [1] relies on exon-exon junction reads to identify retroCNVs. In order to increase their candidate’s reliability, these authors performed experimental validations (Abyzov - Table 2). In summary, the authors. carried out PCR validation for nine putative retroCNVs and for six of them, they found their genomic insertion points (Red blocks). A retroCNV event is, by definition, a retroposition of an mRNA into a genomic region (i.e., it should have an insertion point, otherwise it could be a distinct retroCNV event, even from the same parental gene). Thus, in order to avoid misleading in data comparison, we selected those retroCNVs events validated by PCR and with a defined genomic insertion point.

_images/abyzov_table2.png

Highlighted in red: retroCNVs events presenting an insertion point and with PCR validation. Insertion point coordinates were retrieved from Table X, Abyzov et al, Genome Res, 2013.

Highlighted in blue: a lacking of read depth (RD) support to the candidate CACNA1B.

We called retroCNVs using the same 974 individuals from the fourteen (ASW, CEU, CHB, CHS, CLM, FIN, GBR, IBS, JPT, LWK, MXL, PUR, TSI, and YRI) 1000 Genome populations, which are reported in Supplementary _Table S1. Their six retroCNVs with PCR validation and a defined genomic insertion point (presented above, Abyzov - Table 2) were used. In summary, our pipeline (sideRETRO) identifies five (83.3%) and misses only one retroCNV (CACNA1B). Regarding the genotyping of retroCNVs shared by Abyzov and us, sideRETRO has a match of 70 genotyping out of 70 (100%), See tables below:

RetroCNVs, experimentally validated by PCR and genotyped by Abyzov et al. (2003) and by sideRETRO into individuals from fourteen human populations. TMEM66 (used in Abyzov et al.): now, its official name is SARAF.
Parental Gene Insertion region (GRCh38; chromosome and position)
Abyzov sideRETRO
CBX3 15:40561954-40561998 15:40561980
LAPTM4B 6:166920412-166920482 6:166920475
TMEM66* 1:191829533-191829591 1:191829594
SKA3 11:108714998-108715054 11:108715020
TDG 12:125316536-125316676 12:125316601
CACNA1B 1:148027670-148027843  
Events found by Abzov and sideRETRO are stated as 1/1. Only found by Abyzov: 1/0. Only found by sideRETRO: 0/1. Events absent from Abzov and sideRETRO are stated as 0/0.
Parental Gene Populations
ASW CEU CHB CHS CLM FIN GBR IBS JPT LWK MXL PUR TSI YRI
CBX3 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1
LAPTM4B 0/0 1/1 0/0 0/0 1/1 1/1 1/1 0/0 0/0 0/0 0/0 1/1 1/1 0/0
TMEM66* 0/0 1/1 0/0 0/0 0/0 1/1 1/1 0/0 0/0 0/0 0/0 1/1 1/1 0/0
SKA3 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1
TDG 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1
CACNA1B 1/0 1/0 1/0 1/0 1/0 1/0 1/0 1/0 1/0 1/0 1/0 1/0 1/0 1/0

Regarding the retroCNV event (parental gene CACNA1B; insertion region: chr1: 147499911-147500084) not identified by sideRETRO:

i) Curiously, Abyzov et al. did not find a good Read Depth Support for it (See above, marked in blue and in their manuscript);

ii) We found that its putative insertion region (GRCh37: chr1:147499911- 147500084; GRCh38: chr1:148,027,670-148,027,843) corresponds to a LTR region (Part A- below);

iii) This region has a second (quasi-perfect: only 2 mismatches) hit elsewhere, Part B;

iv) Moreover, this second hit is (suspiciously) near to a fixed retrocopy from the same parental gene, CACNA1B (Figure 1C). SideRETRO filters out retroCNVs (i.e., polymorphic) events inserted near a fixed retrocopy from the same parental gene, because they are usually results from false-positive alignments, since their likelihood of being real is very low (roughly = 1 / (genome size x number of genes; haploid genome: 3x109; the number of genes ~ 20k coding genes). Nevertheless, only a further experimental validation may confirm our hypothesis.

_images/alignment_of_CACNA1B.png

Genome alignment of the CACNA1B region defined by Abyzov et al. A) genomic alignment of the region defined as the insertion point of CACNA1B (in this case, GRCh38 was used). B) The second hit of this sequence into the genome (only two mismatcher in 174bp). C) The 2nd hit into the genome is near a fixed retrocopy from CACNA1B.

Thus, in summary, regarding the genotyping data, our pipeline presents a very good match ranging from 83.3% (considering all events) to 100% (excluding a “suspicious” candidate) against the experimental dataset from an independent group, Abyzov et al. (2013) Gen. Res.

References and Further Reading

[1]Abyzov, Alexej et al. (2013). Analysis of variable retroduplications in human populations suggests coupling of retrotransposition to cell division. Genome Res, 23:2042-52.
[2]Miller, Thiago et al. (2019). galantelab/sandy: Release v0.23 (Version v0.23). Zenodo. http://doi.org/10.5281/zenodo.2589575.
[3]Li H. and Durbin R. (2009). Fast and accurate short read alignment with Burrows-Wheeler Transform. Bioinformatics, 25:1754-60. [PMID: 19451168].