Science Reviews - Biology, 2024, 3(1), 9-15 Martina Elena Tarozzi
Artificial intelligence for Next generation sequencing
data analysis
Martina Elena Tarozzi, indipendent researcher. Florence, Tuscany, Italy
Received March 22, 2024. Revised April 09, 2024. Accepted April 10, 2024.
Abstract: In the rapidly evolving field of genomics, our capacity to decipher genetic data encoded in DNA has
been transformed by Next Generation Sequencing (NGS) technologies. These advanced technologies produce
an enormous volume of data, posing substantial challenges in extracting meaningful biological insights.
Artificial intelligence (AI) algorithms offer distinctive possibilities to unravel the biological information
embedded in such extensive and intricate datasets. This review offers a synopsis of AI classifications and
algorithms, elucidating how these techniques can be employed on sequencing data. Subsequently, a selection of
the most typical, promising, or illustrative applications of AI on NGS data to tackle unresolved technical or
biological issues are showcased.
Artificial intelligence (AI) algorithms and se-
quencing technologies represent two groundbreak-
ing innovations that witnessed outstanding im-
provements in the last few decades. In both AI and
sequencing technologies, the first ilestones date
back to the early ‘50s, and the rapid and simultane-
ous advancements in the two fields resulted in the
new hybrid research branch of computational biol-
ogy[1]. The large and complex datasets produced
by sequencing experiments contain the information
needed to understand many unanswered biological
and medical questions, but that information is often
difficult to extract. As the biomedical sector is be-
coming more data-intensive and AI algorithms
more able to handle biological complexity, the inter-
connection between these two research fields is
bound to strengthen.
Overview of artificial intelligence, machine
learning and deep learning
Artificial Intelligence (AI) is a broad term that
covers a plethora of computational approaches and
algorithms able to mimic cognitive abilities (Figure
1). The term Machine Learning (ML) refers to sev-
eral algorithms able to perform different tasks, such
as pattern recognition, classification and prediction
tasks based on models derived from existing data.
The key feature of these methods is the absence of
coded algorithms given by the developer to de-
scribe the steps towards which input data are trans-
formed in output results. The ML method therefore
learns from the data to create a hierarchy of con-
cepts, each one defined by its relation to simpler
concepts, that it uses to perform a task. By building
knowledge from previous data and training, this
approach avoids the need for the developer to ex-
plicitly define all the knowledge that the machine
needs. The goal of many ML tasks is to optimize the
model performance so that they can be generalized
on independent datasets (generalization perfor-
Martina Elena Tarozzi Science Reviews - Biology, 2024, 3(1), 9-15
Figure 1: Schematic representation of differences and shared features between Artificial Intelligence (AI), Machine
Learning (ML) and Deep Learning (DL).
ML methods are usually defined either as su-
pervised or unsupervised. In unsupervised learn-
ing, no predefined labels are provided for the ob-
jects under study. Here, the goal is to explore the
data and discover similarities between objects (e.g.,
samples) based solely on the input data. Clustering
and most dimensionality reduction techniques rep-
resent examples of unsupervised algorithms. Unsu-
pervised methods are commonly used in explora-
tory data analysis and for quality control tasks to
identify potential issues such as outliers and batch
effects, as well as to discover recurrent patterns and
unknown sources of variation in highly dimen-
sional datasets [2,3]. Examples of unsupervised di-
mensionality reduction techniques are principal
component analysis (PCA), t-distributed stochastic
neighbor embedding (t-SNE)[4] and Uniform Man-
ifold Approximation and Projection (UMAP) [5].
PCA is a fast, linear transformation, which maps the
data over a space whose coordinates are linear com-
binations of input features that capture most of the
variance of the data. This algorithm is usually pre-
ferred when the aim is to separate the data points as
far as possible. On the other hand, methods such as
UMAP or t-SNE rely on more complex
mathematical assumptions and steps, to “guess” the
manifold on which data are located. Such methods
preserve only local similarities and hence produce a
higher clusterization of the data in the embedding
space, with data subpopulations more separated
among themselves compared to PCA embeddings
[6,7]. Clustering methods are used to identify in an
unsupervised way groups of similar data points
based on the measure of similarity of choice. Exam-
ples of commonly used clustering algorithms in se-
quencing data analysis are hierarchical clustering
with dendrograms, K-means and Density-Based
Spatial Clustering of Applications with Noise
(DBSCAN) [8].
On the other hand, supervised learning in-
volves using labeled data, where each input has an
associated output. This allows the algorithm to
learn a set of rules to predict the correct output for
new input data based on its features, attributes, or
labels. If the output is qualitative, then the process
is called classification [9,10]. Otherwise, in the case
of quantitative values, it is called regression. Well-
trained ML models can learn rules about the under-
lying patterns and relationships in the data that can
then be applied to make accurate predictions on
Science Reviews - Biology, 2024, 3(1), 9-15 Martina Elena Tarozzi
new, unseen data. These rules can display new in-
sights into the relevant features used to correctly
identify the studied classes[11]. Examples of classi-
fiers are decision trees, random forests, support vec-
tor machines and neural networks. Examples of re-
gression methods are linear or generalized linear
models, with the possible addition of random ef-
fects (mixed-effect models), penalization terms
(regularized models), non-linearity (e.g., kernel re-
gression), or basis functions (e.g., splines). Never-
theless, it is worth underlying that supervised and
unsupervised learning are not formally defined
terms, and the boundaries between them are often
blurred since many ML methods can be used to per-
form both types of tasks[12].
Neural networks are ML algorithms consist-
ing of interconnected artificial neurons, which rep-
resent the building blocks of neural networks and
deep learning algorithms[13]. Artificial neurons
have as input a vector of values and compute
weighted sums of these values followed by a non-
linear transformation. The activation function de-
fines whether the input values reach the threshold
needed to activate the neurons and consequently
determines the output of the node given the set of
inputs received. The weights are parameters that
are adjusted during the training step, as learning
proceeds[11]. The term Deep Learning (DL) refers
to multi-layered artificial neural networks with a
high number of hidden layers used to extract pro-
gressively higher-level features from data. The first
layer of neurons, also referred to as the input layer,
is the one that receives the experimental input data,
followed by layer two, made of neurons that receive
as input the outputs of layer one, and so forth for
deeper hidden layers[13]. The goal of these
networks is to model a function f. In a classifier, for
example, the function y = f (x) maps an input x to a
category y. A feed-forward network defines a map-
ping y = f (x; θ) and learns from the training set the
value of the parameters θ to model the function.
Training a neural network involves optimizing its
parameters (weights) to minimize a certain error
metric, which is typically defined by a loss function.
Such loss function measures the difference between
the predicted output of the neural network and the
actual output. Optimal weight updates are enabled
by backpropagation, which is a well-established al-
gorithm in neural network training, as it enables the
computation of the loss function's gradient with re-
spect to the network's weights.
Neural networks can be categorized into three
main types based on the type of architecture in
which neurons are organized: feed-forward, recur-
rent, and convolutional (Figure 2). In feed-forward
networks, the connections between nodes do not
form a cycle, and the information proceeds exclu-
sively forward from the input layer to the hidden
layers and lastly to the output layer. On the contrary,
recurrent neural networks have connections that
form cycles, allowing the output of a node to affect
subsequent input to the same nodes. Convolutional
neural networks are made of convolution kernels
processing input data, followed by pooling layers
simplifying the information to its most meaningful
concepts, and ultimately followed by hidden fully
connected layers for further data processing, like for
example a prediction task. The ultimate output of
the neural network represents its prediction or clas-
sification of the input data, which is built based on
its experience of recurrent patterns learned from the
Figure 2: Schematic representation of the three main types of neural networks: feed-forward neural network (a), recurrent neural
networks (b) and convolutional neural networks (c). In all three representations, orange circles stand for the input neurons and
green circles for the output neurons. In panel a, blue circles represent the hidden layers. In panel b, light blue circles indicate
recurrent hidden layers, while in panel c, yellow circles indicate the kernels and the gray 3D squares represent convolutional layers.
Martina Elena Tarozzi Science Reviews - Biology, 2024, 3(1), 9-15
Applications of AI in genomics and transcriptomics
The complexity of data generated by high-
throughput sequencing technologies can make tra-
ditional analysis methods insufficient for identify-
ing patterns and extracting insights.ML and DL
methods have been applied to sequencing data with
a vast number of scopes. Here we provide a selec-
tion of some of the most relevant fields of applica-
tion. This section aims at providing common, prom-
ising or exemplifying applications of AI methods on
NGS data in biology and bioinformatics, while it
should not be considered a complete overview of all
its possible applications in biology.
Liquid biopsies and personalized medicine
Liquid biopsies are minimally invasive diagnostic
methods that analyze bodily fluids, such as blood,
urine, or cerebrospinal fluid, to detect and monitor
diseases, and are especially relevant in early diag-
nosis of cancer and neurodegenerative diseases
[14,15]. These samples contain cell-free Nucleic Ac-
ids (cfNA), circulating tumor DNA (ctDNA), circu-
lating tumor cells (CTCs), exosomes, and other bi-
omarkers that allow the extraction of genomic, tran-
scriptomic and epigenomic information, which can
be used for early detection, monitoring of progres-
sion and support personalized therapeutic deci-
sions to target the disease [15]. These types of data
are extremely complex, subject to many confound-
ers and for most features characterized by a high
signal-to-noise ratio. AI algorithms have signifi-
cantly advanced data analysis and interpretation of
this data and consequently the whole field in sev-
eral aspects, such as in risk assessment and early di-
agnosis [16], disease subtype classification [17],
treatment response prediction [18] and in monitor-
ing minimal residual disease [19]. For example,
SVM were effectively used to predict the probabil-
ity of reoccurrence based on gene expression data
or specific gene signature in different types of can-
cers[20,21], improving the monitoring of the molec-
ular profile of the patient’s tumor and the predic-
tion of personalized treatments at different times.
Furthermore, ctDNA methylation patterns have
been extensively studied with several ML classifica-
tion or regression methods as well as with neural
networks to achieve effective early detection both in
cancer research[22] and in the context of neuro-
degenerative diseases[23]. In this context, AI
reaches some of the most notable results in terms of
tangible impacts in molecular biology and medicine,
and it is expected that its role in personalized med-
icine will increase in the near future.
Regulatory genomics
Regulatory genomics is the field of genomics that
studies gene expression regulation trying to iden-
tify regulatory regions (such as enhancers, promot-
ers, transcription start sites (TSS), and genome ac-
cessibility) and the regulatory hierarchy between
these regions and other genes. In this context, deep
learning and more specifically Convolutional Neu-
ral Networks have been applied with the best re-
sults. One of the commonly used architectures in-
volves treating the input DNA sequence as categor-
ical variables. Each position in the sequence is one-
hot encoded, resulting in a vector where only one
channel corresponds to the A-C-G-T nucleotides
(with a value of 1) provided to the input layer.
These kernels are followed by convolutional layers,
which simplify the information to extract the most
relevant concepts. Convolutional filters are initially
trained on specific regions of interest with known
regulatory properties. The knowledge gained by
the convolutional neural network (CNN) during
training can then be applied to new regions for ac-
curate predictions. This architecture has been suc-
cessfully applied to various types of sequencing
data, particularly in the context of epigenomic stud-
ies. This overall architecture has been used on dif-
ferent types of sequencing data and has provided
particularly interesting results in terms of epige-
nomic studies. For example, this type of architec-
ture has been applied to DNAase-seq data to pre-
dict cell-type specific regions of accessible chroma-
tin [24], to identify promoters and distal regulatory
regions along mammalian genomes [25], to predict
cell-type specific gene expression from DNA se-
quencing data and alterations of it associated to var-
iant alleles [26], and to identify genomic regions re-
sponsible for the three-dimensional chromatin fold-
ing in the nucleus [27] from genomic and Hi-C data.
Considering that both the experimental and compu-
tational technologies used in these studies are rela-
tively young, this is arguably one of the most prom-
ising research fields for the next decades, with the
potential to answer many of the open questions in
functional genomics.
Improvement of genome editing specificity
Science Reviews - Biology, 2024, 3(1), 9-15 Martina Elena Tarozzi
During the past decade, technological innovations
in molecular biology have made genome editing
easier, allowing the modification of the DNA se-
quence at a single nucleotide resolution[28]. The
most successful technique to perform genome edit-
ing is CRISPR/cas9, where the identification of the
target genomic region is mediated by a guide RNA
(gRNA). The gRNA is a chimeric RNA consisting of
a ~20nt guide sequence that identifies through base
complementarity the target site in the genome and
precisely directs the Cas9 protein to it. Some mis-
matches in the guide sequence can be tolerated and
do not affect the ability to align and cut DNA, re-
sulting in off-target cleavages [29,30]. Accurate
gRNA design maximizes on-target efficacy (sensi-
tivity) and minimizes off-target effects (specificity).
ML and DL models have been used in this context
to predict gRNA sequencing with high sensitivity
and specificity, and several specific tools have been
released in the last few years, for example, Deep-
SpCas9 [31], DeepCRISPR [32] , DeepCpf1 [33],
CRISPRscan [34], among many other. These tools
differ in terms of models and network architectures,
nevertheless, the fundamental overall architecture
described in the previous section still applies also in
this context. Of course, other more complex net-
works exist, like the one used by DeepCRISPR[32],
where a hybrid deep neural network is used com-
bining unsupervised and supervised representation
learning to model single gRNAs using a set of ge-
nome-wide sgRNAs. Despite the outstanding re-
sults already achieved, this type of application of
ML and DL on sequencing data still has some rele-
vant limitations [35]. In this context, the large
amount of data needed to train prediction models is
not always available, and quite often the available
data show some challenges caused by the heteroge-
neity of sequencing platforms and cell types.
Future Perspectives
Both computational and molecular biology
are continuing to grow at an impressive pace, as
they did in the last decades. As shown in this review,
integrating AI, ML and DL techniques with NGS
data holds immense promise, but it also presents
challenges. For example, the quality and quantity of
NGS data pose hurdles e.g. noise, biases, and ar-
tifacts- that can impact analyses and computational
resources and strain existing infrastructure due to
DL model demands. In addition, these models re-
quire a huge amount of data which is not always
available, therefore one of the currently most rele-
vant challenges in the field is addressing small sam-
ple sizes and class imbalance affects model robust-
ness. Another limitation is that interpretability re-
mains an issue: black-box models lack transparency
and the understanding of the molecular processes
underneath a given prediction or classification
based on nucleotides pattern often remains under
understood. Bridging the gap between AI expertise
and biological domain knowledge is critical and
could improve transferability and generalization
across diverse biological contexts as well. Last but
not least, ethical concerns arise from handling sen-
sitive genomic data. Finding a balance between the
extraordinary potential of AI in the medical field
and the need for a careful safeguard of individual
privacy are really hot topic involving both law and
tech experts which currently remains an open issue.
In summary, while AI, ML, and DL offer exciting
prospects for genomics research, overcoming these
challenges is crucial for realizing their full potential.
Taken together these innovations are a new frontier
of research and have the potential to strongly affect
our possibilities to understand and interact with ge-
netic information, which will also require ethical
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