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Below we provide code for non-standard analyses. This repo also contains all data required to reproduce the analyses.

  • Putative horizontal gene transfer
  • Create database for adaptive sequencing
  • Analysis of experiment that switched adaptive sequencing on and off
  • Bayesian regression model of target count



conda create -n adaptive -y -c bioconda mummer mmseqs2 prodigal samtools minimap2
conda activate adaptive
pip install screed tqdm numpy pandas matplotlib 

# This could be installed in a separate environment to speed up installations:
conda install -c r r-tidyverse


All basecalled nanopore sequencing data has been deposited with the SRA, NCBI. Metagenomic reads are available under project ID PRJNA788147. Reads from isolate genomes, the Flongle flow cell and the experiment alternating between adaptive and standard state have been deposited under project ID PRJNA788148 under their respective sample ID (Raoultella ornithinolytica: SAMN23928631, Citrobacter freundii: SAMN23928632, Citrobacter amalonaticus: SAMN23928633).

To make download the sequencing data more convenient, we mirrored the SRA data on OSF for batch download:

# Clone code and required data from repo
git clone
cd adaptive

# Download raw sequencing data from OSF project
pip install osfclient
osf -p 8p9t4 clone
mv 8p9t4/osfstorage/sequencing .
# This should create a folder "sequencing" that contains the raw seq data.
  • A2 .. Raoultella ornithinolytica
  • B1 .. Citrobacter freundii (same as A1, not used here)
  • B2 .. Citrobacter amalonaticus

Putative horizontal gene transfer

Pairwise align isolate genomes and MAGs and parse all genes longer than 1 kb and 99% sequence identity.

from glob import glob
from itertools import combinations, islice
import subprocess
import os
from pathlib import Path

import screed
from tqdm import tqdm

assemblies = [Path(f'assemblies/{i}.fna') for i in ['A2', 'B1', 'B2']]
d = {'.fna', ''): str(i.resolve()) for i in assemblies}

bins = Path('metagenome/MAGs_uncultured/')
d2 = {'.fa', ''): str(i.resolve()) for i in bins.glob('*.fa')}
d2 = {k.replace('.', '_'): v for k, v in d2.items()}

# Pairwise genome alignment
p = Path('tmp/')
p.mkdir(parents=True, exist_ok=True)

for i, j in tqdm(combinations(d, 2)):
    name = f'{i}__{j}'  # dunder
    print(name)['nucmer', '-p', f'tmp/{name}', d[i], d[j]])

Now retrieve all shared elements with a minimum:

  • alignment nucleotide identity (0.999)
  • alignment length (1 kb)
for i in $(find tmp -name '*.delta'); do
    filename=$(basename -- "$i")
    echo $name
    dnadiff -p tmp/${name} -d $i
    # Create a bed for all shared entries with a homology > 99.9%
    cut -f1,2,7,12 tmp/${name}.mcoords | \
    awk -v 'OFS=\t' '{print $4,$1,$2,$3}' | \
    awk '$4>=99.9' > tmp/${name}.bed
from glob import glob
from itertools import product
import os

import numpy as np
import pandas as pd

import matplotlib
import matplotlib.pyplot as plt

# params
minlen = 1000

files = glob('tmp/*.bed')
m = {}  # matrix

for i in files:
    c1, c2 = os.path.basename(i).replace('.bed', '').split('__')
        df = pd.read_csv(i, sep='\t', header=None)
        cnt = 0
        for _, j in df.iterrows():
            if j[2] - j[1] > minlen:
                cnt += 1
        m[(c1, c2)] = cnt

    except pd.errors.EmptyDataError:
        m[(c1, c2)] = 0

ix = sorted(set([item for sublist in m.keys() for item in sublist]))
M = np.zeros([len(ix), len(ix)])

for n, i in enumerate(ix):
    for c1, c2 in product([i], ix):
        if not c1 == c2:
                M[n, ix.index(c2)] = m[(c1, c2)]
            except KeyError:
                M[n, ix.index(c2)] = m[(c2, c1)]

# Zero all entries below the diagonal, bc/ matrix is symmetric
M = np.triu(M)

colors ='gray_r')
labels = ['C. freundii', 'R. ornithinolytica', 'C. amalonaticus', 'E. faecium (MAG)', 'S. ureilytica (MAG)']

fig = plt.figure()
fig.set_size_inches(4, 2)
ax = fig.add_subplot(111)
cax = ax.matshow(M, interpolation='nearest', cmap=colors)


ax.set_xticklabels([''])  # ix
ax.set_yticklabels([''] + labels)  # ix

plt.savefig('matrix.png', dpi=300)

Create database for adaptive sequencing

# For details see database/
mmseqs easy-cluster --min-seq-id 0.95 -c 0.8 --cov-mode 1 database/nucleotide_fasta_protein_homolog_model.fasta cluster tmp

Analysis of experiment that switched adaptive sequencing on and off

Aim: Extract all open reading frames marked as antimicrobial resistance gene. Then map reads from the experiment in both conditions ("adaptive", "standard") to them, compare.

mkdir aln
for i in A2 B1 B2; do
    prodigal -i assemblies/${i}.fna -a assemblies/${i}.faa -d assemblies/${i}.coding.fna > /dev/null 
    mmseqs easy-search --threads 8 --max-accept 1 --min-seq-id 0.9 --cov-mode 1 -c 0.8 assemblies/${i}.faa $DB assemblies/${i}.m8 assemblies/tmp
    cut -f1 assemblies/${i}.m8 > select
    seqtk subseq assemblies/${i}.coding.fna select > assemblies/${i}.amr.fna
    rm select

    DATA=$(find sequencing/adaptive_on_off -name '*.fastq.gz' | grep $i)

    mkdir aln/${i}
    for QRY in $DATA; do
        filename=`basename $QRY`
        minimap2 -ax map-ont --secondary=no -t 8 $REF $QRY | grep -v '^@' > aln/${i}/${filename}.sam

How similar are the coding sequences we annotated as resistance genes to their respective matches in the reduced AMR database we used during adaptive sequencing?

for i in A2 B1 B2; do
    # --search-type 3 means nucleotide search
    # Note: We search against reduced database, thus more lenient params
    mmseqs easy-search --search-type 3 --threads 8 --max-accept 1 --min-seq-id 0.5 --cov-mode 1 -c 0.5 $QRY $DB assemblies/${i}.amr.card_sim.m8 assemblies/tmp
# Sanity check:
# grep '>' assemblies/B2.amr.fna | wc -l
# 44
# wc -l assemblies/B2.amr.card_sim.m8
# 44

Integrate data.

from collections import defaultdict
import json
from pathlib import Path

import numpy as np
import pandas as pd
import screed
from tqdm import tqdm

def parse_fp(fp):

    ('standard', 'B1', 1, 11)
    x ='h.fastq.gz.sam')
    if 'unrejected' in x:
        group = 'adaptive'
        # unrejected_A2_2_16h.fastq.gz
        _, isolate, replicate, hours = x.split('_')
        group = 'standard'
        # A2_1_7h.fastq.gz
        isolate, replicate, hours = x.split('_')
    return group, isolate, int(replicate), int(hours)

Look at assembly graph (bandage, assemblies/*.gfa) and mark circular
chromosome, assuming all else is non-chromosomal.

Also, we note the coverage in the assembly, as a proxiy for relative copy number
of the contigs (for use later in a regression model, see misc/cn.json).

# Which contigs belong to the chromosome?
chromosome = {
    'A2': 'contig_1',
    'B1': 'contig_1',
    'B2': 'contig_2',

# What is their copy number?
with open('misc/cn.json', 'r') as file:
    cn = json.load(file)

# Collect information on which reads map to which ORF in which condition,
# how long the reads were, etc.
cnts, cnt_lens = {}, {}
read_lens = defaultdict(list)

for sample in ['A2', 'B1', 'B2']:
    cnt = defaultdict(dict)
    cnt_len = defaultdict(dict)
    df = pd.read_csv(f'assemblies/{sample}.m8', sep='\t', header=None)
    amr = set(df[0])  # AMR detected in isolate

    p = Path(f'aln/{sample}')
    for fp in tqdm(p.rglob('*.sam')):

        # How many reads map to AMR genes?
        with open(fp, 'r') as file:
            for line in file:
                l = line.strip().split('\t')
                contig = l[2]
                read_len = len(l[9])

                if (contig != '*') and (contig in amr):

                    group, isolate, replicate, hours = parse_fp(fp)
                    # Read len distr chromosome vs plasmid
                    if '_'.join(contig.split('_')[:2]) == chromosome[sample]:

                        cnt[group][contig] += 1

                    except KeyError:
                        cnt[group][contig] = 1               # initialize
                        cnt_len[group][contig] = [read_len]  # initialize
    cnts[sample] = cnt
    cnt_lens[sample] = cnt_len

# Load similarity vals we calculated above for the ORFs.
sims = {}
for sample in ['A2', 'B1', 'B2']:
    sim = {}
    fp = f'assemblies/{sample}.amr.card_sim.m8'
    df = pd.read_csv(fp, sep='\t', header=None)
    for _, i in df.iterrows():
        sim[i[0]] = i[2]
    sims[sample] = sim

# Each line one gene (coding sequence) with properties of the same.
with open('misc/similarity.csv', 'w+') as out:
    # results = []
    for sample in ['A2', 'B1', 'B2']:
        for condition in ['standard', 'adaptive']:
            for k, v in cnts[sample][condition].items():
                # 'contig_2_3755': 43,
                coding = '_'.join(k.split('_')[:2])  
                # contig_2_34 to contig_2
                if coding == chromosome[sample]:
                    carrier = 'chromosome'
                    carrier = 'plasmid'

                cov = cn[sample][coding]  # coverage
                mu = np.median(cnt_lens[sample][condition][k])
                s = sims[sample][k]



df <- read_csv('misc/similarity.csv')
df$log_mu = log(df$mu)

p <- ggplot(df, aes(x=carrier, y=count, color=similarity)) + geom_jitter(size=.8) + theme_minimal() + theme(axis.text.x=element_text(angle=45, vjust=1, hjust=1)) + ylab('read count') + scale_colour_viridis_c() + facet_wrap(~condition)
ggsave('plot3.pdf', p, width=7, height=10, units='cm')
# Figure 2B

palette <- 'Accent'
p <- ggplot(df, aes(x=carrier, y=log_mu, color=condition)) + geom_jitter(size=.75, position = position_jitterdodge()) + geom_boxplot(alpha=0.75, outlier.shape=NA) + theme_minimal() + theme(axis.text.x=element_text(angle=45, vjust=1, hjust=1)) + ylab('log median read length') + scale_color_brewer(palette=palette)
# Figure 2D

Bayesian regression model of target count

# Pull container with all dependencies, enter it and start R session
docker pull lcolling/brms
docker run --rm -it -v $PWD:/data lcolling/brms /bin/bash




df <- read_csv('/data/misc/similarity.csv')
df$adaptive <- ifelse(df$condition == 'adaptive', 1, 0)
df$plasmid <- ifelse(df$carrier == 'plasmid', 1, 0)
df$log_mu = log(df$mu)

# Standardize coverage predictor to make model fit easier
df2 <- df %>% mutate_at(c('coverage'), ~(scale(.) %>% as.vector))

m <- brm(
    count ~ 1 + similarity*adaptive + log_mu*plasmid + log_mu*adaptive + coverage
# posterior_summary(m)

# Merge posterior samples with predictors for visualisation.
new <- fitted(m, newdata=df2) %>% as_tibble() %>% bind_cols(df2)

palette <- 'Accent'
p <- ggplot(new, aes(x=similarity, y=count, color=condition)) + geom_point(size=.8) +geom_smooth(data=new, aes(y=Estimate, ymin=Q2.5, ymax=Q97.5, fill=condition)) + theme_minimal() + scale_color_brewer(palette=palette) + scale_fill_brewer(palette=palette) + ylab('read count')
# Figure 2C

p <- ggplot(new, aes(x=log_mu, y=count, color=condition)) + geom_point(size=.8) +geom_smooth(data=new, aes(y=Estimate, ymin=Q2.5, ymax=Q97.5, fill=condition)) + theme_minimal() + xlab('log median read length') + scale_color_brewer(palette=palette) + scale_fill_brewer(palette=palette) + ylab('read count')
# Figure 2E

Model summary:

 Family: poisson
  Links: mu = log
Formula: count ~ 1 + similarity * adaptive + log_mu * plasmid + log_mu * adaptive + coverage
   Data: df2 (Number of observations: 238)
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup samples = 4000

Population-Level Effects:
                    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept               5.77      0.14     5.50     6.04 1.00     1999     2395
similarity             -0.09      0.12    -0.32     0.13 1.00     2407     2466
adaptive               -9.65      0.18   -10.01    -9.30 1.00     1540     1740
log_mu                 -0.03      0.01    -0.04    -0.02 1.00     1856     2444
plasmid                -0.41      0.07    -0.54    -0.28 1.00     1516     2096
coverage                0.05      0.00     0.04     0.05 1.00     4418     2890
similarity:adaptive    11.98      0.15    11.68    12.28 1.00     1665     1904
log_mu:plasmid          0.06      0.01     0.05     0.08 1.00     1456     1861
adaptive:log_mu        -0.22      0.01    -0.23    -0.20 1.00     2044     2000

Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).


Supporting information for a study on adaptive nanopore sequencing







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