<table><caption>Table E.5 – Reminder of the number of miRNAs per class, then the number of motifs inferred on SeqEᵢ with a match on miRNA *i*, and finally the number of motifs inferred on SeqEᵢ with a match on miRNA *i*.</caption><thead><tr><th colspan="4">Human dataset</th></tr><tr><th>Class</th><th>#miRNAs</th><th>#motifs with a match</th><th>#motif with p-value &lt;= 5e-5 )</th></tr></thead><tbody><tr><td>All</td><td>316</td><td>292</td><td>109</td></tr><tr><td>I</td><td>158</td><td>150</td><td>81</td></tr><tr><td>II</td><td>163</td><td>152</td><td>57</td></tr><tr><td>III</td><td>178</td><td>162</td><td>49</td></tr><tr><td>IV</td><td>142</td><td>131</td><td>50</td></tr><tr><td>V</td><td>201</td><td>183</td><td>6</td></tr></tbody></table><table><thead><tr><th colspan="4">Mouse dataset</th></tr><tr><th>Class</th><th>#miRNAs</th><th>#motifs with a match</th><th>#motif with p-value &lt;= 5e-5 )</th></tr></thead><tbody><tr><td>All</td><td>299</td><td>277</td><td>31</td></tr><tr><td>1</td><td>129</td><td>118</td><td>2</td></tr><tr><td>2</td><td>166</td><td>154</td><td>12</td></tr><tr><td>3</td><td>150</td><td>140</td><td>11</td></tr><tr><td>4</td><td>96</td><td>90</td><td>4</td></tr><tr><td>5</td><td>131</td><td>118</td><td>3</td></tr><tr><td>6</td><td>178</td><td>167</td><td>22</td></tr></tbody></table>

to retrieve miRNA/mRNA interaction patterns as defined by the clustering of each class (see Section C.2) that are not the seed by searching only on the mRNAs. This is rather encouraging since the methods based on pattern recognition (see Section iii) were focused on the seed.

## Specificity of the motifs inferred at the interaction sites of each given miRNA

Until now, we were always comparing the motifs found on SeqEᵢ with their respective miRNA *i*. We were able to find a specific interaction pattern, however another question appeared: are we able to link the motifs inferred from SeqEᵢ with the miRNA *i* or the motif has a better score with another miRNA?

To answer this question, we used FIMO again to compare the motifs with the sequences of all miRNAs, including the one (miRNA *i*) experimentally identified. We only selected the best match per miRNA when FIMO found more than one occurrence. We then sorted all miRNAs by their p-values and considered the miRNA *i* as retrieved when it was ranked first by FIMO.

In the majority of the tests, the correct miRNA *i* is not the closest to the motif found when looking for all occurrences. However, when selecting occurrences with a p-value below 5e-5, we partially managed to retrieve miRNA *i* from all the miRNAs in the human dataset especially for Class I. In the mouse dataset, the number of times we retrieved the correct miRNA was very low, even selecting occurrences with a p-value below 5e-5 (see Tables E.6).

In the human dataset, the results for Class V are the worst. This is expected due
<figure><img src="image_76.png" /><figcaption>Figure E.12 – Heatmap for each class of motifs found on the human SeqEᵢ dataset and their placement on their respective miRNA *i*. On the left column: all motif placements on their respective miRNA. On the right column: only the motifs that have a corresponding p-value as given by FIMO that is less or equal to 5e⁻⁵.</figcaption></figure>
<figure><img src="image_77.png" /><figcaption>Figure E.13 – Heatmap for each class of motifs found on the mouse SeqEᵢ and their placement on their respective miRNA *i*. On the left column: all motif placements on their respective miRNA. On the right column: only the motifs that have a corresponding p-value as given by FIMO that is less or equal to 5e⁻⁵.</figcaption></figure>
<table><caption>Table E.6 – Number of times the motifs generated from $Seq_{E_i}$ on the mouse and the human datasets have the best similarity with miRNA *i*.</caption><thead><tr><th rowspan="2">Class</th><th colspan="3">All occurrences</th><th colspan="3">Human dataset</th></tr><tr><th>first rank of miRNA <i>i</i></th><th>Top 10 of miRNA <i>i</i></th><th>#motifs</th><th>first rank of miRNA <i>i</i></th><th>Top 10 of miRNA <i>i</i></th><th>#motifs</th></tr></thead><tbody><tr><td>Total</td><td>83</td><td>152</td><td>316</td><td>76</td><td>122</td><td>183</td></tr><tr><td>I</td><td>68</td><td>106</td><td>158</td><td>63</td><td>90</td><td>100</td></tr><tr><td>II</td><td>45</td><td>94</td><td>163</td><td>40</td><td>68</td><td>90</td></tr><tr><td>III</td><td>46</td><td>90</td><td>178</td><td>38</td><td>61</td><td>92</td></tr><tr><td>IV</td><td>40</td><td>87</td><td>142</td><td>31</td><td>61</td><td>73</td></tr><tr><td>V</td><td>3</td><td>27</td><td>201</td><td>2</td><td>8</td><td>60</td></tr></tbody></table><table><thead><tr><th rowspan="2">Class</th><th colspan="3">All occurrences</th><th colspan="3">Mouse dataset</th></tr><tr><th>first rank of miRNA <i>i</i></th><th>Top 10 of miRNA <i>i</i></th><th>#motifs</th><th>first rank of miRNA <i>i</i></th><th>Top 10 of miRNA <i>i</i></th><th>#motifs</th></tr></thead><tbody><tr><td>Total</td><td>33</td><td>103</td><td>299</td><td>17</td><td>50</td><td>119</td></tr><tr><td>1</td><td>7</td><td>38</td><td>129</td><td>2</td><td>5</td><td>21</td></tr><tr><td>2</td><td>19</td><td>57</td><td>166</td><td>9</td><td>19</td><td>46</td></tr><tr><td>3</td><td>14</td><td>44</td><td>150</td><td>8</td><td>18</td><td>44</td></tr><tr><td>4</td><td>9</td><td>18</td><td>96</td><td>4</td><td>4</td><td>9</td></tr><tr><td>5</td><td>7</td><td>25</td><td>131</td><td>0</td><td>8</td><td>30</td></tr><tr><td>6</td><td>31</td><td>57</td><td>178</td><td>18</td><td>28</td><td>52</td></tr></tbody></table>

to the distributed nature of the interaction pattern of this class. The worst class in the mouse dataset is also Class 5, which could be explained by the non-paired pattern of the nucleotide 15 and around.

It is worth mentioning that the miRNAs in human are far better retrieved than those in mouse. In the mouse dataset, the correct miRNA is rarely ranked first. However, it is often among the best top 10. The results are shown in Table E.6 and the distribution of the rank of the miRNA is presented in Figure E.14.

## E.3 Prediction of the interaction sites using intra-species conservation

In this section, we are gradually injecting the previous knowledge gathered in order to help improve the discovery of new interactions for an already known miRNA, or even point to the existence of new miRNAs not yet identified.

The performance of the motif predictions was also evaluated using the precision, recall and F-score metrics for the miRNAs with, respectively, at least two and at least ten targets, and we considered the occurrence of a given motif predicted by MEME as a **strong true positive**, $TP_{strong}$ for short, when it is **fully included within the site given by CLASH**. When the occurrence of a predicted motif overlapped the site given by CLASH without being fully included in it, this was considered as a weaker type of true positive and we therefore chose to also consider it separately. We named it **weak true positive**, $TP_{weak}$ for short.

Considering the parameters used by MEME for motif prediction and the constraint of having only one solution, the false positives, FP for short, and false negatives, FN for short, have the same value. Notice that in consequence, the recall and the precision metrics also have the same value. We decided to only show the F-score.
<figure><img src="image_78.png" /></figure>

<figure><img src="image_79.png" /><figcaption>(a) On human dataset, with all occurrences</figcaption></figure>

<figure><img src="image_80.png" /><figcaption>(b) On mouse dataset, with all occurrences</figcaption></figure>

<figure><img src="image_81.png" /><figcaption>(c) On human dataset, only occurrences
with a p-value $<= 5e-5$</figcaption></figure>

<caption>(d) On mouse dataset, only occurrences
with a p-value $<= 5e-5$</caption>

Figure E.14 – Distribution of the rank of the miRNA *i* against all other miRNAs when comparing the motif inferred in *Seq*<sub>*E*<sub>*i*</sub></sub>.
i Searching in noisier sequences for the interaction sites

In a similar way to the comparison of methods carried out in Chapter D, we cre-
ated different Seq<sub>W<sub>i</sub></sub> datasets according to the numbers of chimeric reads supporting each
interacting sequence.

We then computed on the Seq<sub>W</sub> datasets the F-score for all occurrences of all motifs for each class and region of the mouse and for each class for the human. We gathered the results in Figure E.15. Notice that in this experiment, we are scoring the placement of the occurrences independently of the motif.

The results obtained are independent of the class, region or depth of the reads. It is
around 10%. An F-score of 0.10 indeed corresponds to 10% of occurrences that are inside
the interaction sites. Moreover, the average width of the interaction sites is roughly 45
nucleotides so the width of a sequence of Seq<sub>W</sub> is around 445 nucleotides, which means
that a random occurrence has a 10% probability of being well placed.

All F-scores in relation to the mouse dataset are around 0.1, therefore not better than a randomly picked position. In relation to the human dataset, the F-scores are around 0.2 with a peak at around 0.3 for Class I which is only slightly better than random picking. Class V in the human is nonetheless around 0.1. Overall, these results are equivalent to a random picking and we decided to put aside the depth of the reads in the following experiments since it massively increases the number of datasets and with it the computational cost without adding any information at the current stage of the study.

Additionally, we wanted to understand from the previous results if the true positives
are coming from the occurrences of a few motifs or rather globally from the few occurrences
coming from all motifs that are true positives. In other words, we wanted to analyse the
performance of the motifs instead of their occurrences.

Therefore, we computed the F-score for each motif individually to verify the distri-
bution of these metrics (see Figure E.16). We checked the F-score computed on the
occurrences of a motif, which emphasises the fact that the majority of the motifs, as
expected, do not correspond to the interaction sites but to other conserved parts of the
sequences. There are, however, a few motifs that have near perfect F-scores, which means
that they correspond at least partially to interaction sites. These conserved motifs are
as expected once again to be found more in the first three classes of the human dataset
(because of the seed) and less so in relation to Classes 1 and 2 in the mouse dataset.

ii Performance of the best motif

To further explore the approach of intra-species, we needed to refine the experiment
and test if focusing only on the best motifs inferred on Seq<sub>E<sub>i</sub></sub> and that retrieve the miRNA
*i* when compared to all other miRNAs as described in Section iii would increase the F-
score in relation to the Seq<sub>W<sub>i</sub></sub> dataset. Furthermore, we used only motifs that have at
least an occurrence with p-value <= 5e-5. We call such motifs θ<sub>i</sub>

The results of this evaluation are displayed in Figure E.17.

In the mouse dataset, there are only a handful of motifs θ_i (see Table E.5). However, five of them had a perfect F-score with two in the Class total, one in Class 3 and two in Class 6. In Class 6, while the F-score of the motifs θ_i is not perfect it is rather good (around 0.8). In the other classes 2, 3 and 5, they are not very good, with an F-score around 0.5 or worse.
<figure><img src="image_82.png" /><figcaption>(a) MEME motifs inferred in the mouse SeqWᵢ for each (b) MEME motifs inferred in the human SeqWᵢ for each class.</figcaption></figure>

<caption>Figure E.15 – MEME motifs inferred in SeqWᵢ.</caption>
<figure><img src="image_83.png" /><figcaption>Figure E.16 – Histogram representing the number of motifs by F-score on $Seq_{W_i}$ for each dataset and for each class. In orange are the datasets with at least 2 targets, and in green those with at least 10 targets. Note that the green dataset is a subset of the orange one which implies that the green number of miRNAs is equal or smaller than the orange one.</figcaption></figure>
<figure><img src="image_84.png" /><figcaption>Figure E.17 – Distribution of the F-score when searching on Seq<sub>W<sub>i</sub></sub> with motifs θ<sub>i</sub>.</figcaption></figure>

In the human dataset, there are more motifs θᵢ compared to the mouse one and this
analysis identifies 21 motifs θᵢ with a perfect F-score (respectively three, eight, six, three,
and one in the Class total and then Classes I to IV. When they are not perfect, their
scores are still very good, around 0.9 for Classes I and II and around 0.75 for Classes III
and IV.

iii Performance on complete mRNAs

Our final test was to assess the performance of the motifs θᵢ on an even noisier dataset.
Hence, in relation to the dataset SeqWᵢ, we created the dataset SeqCᵢ that gathers all the
complete mRNAs that are targeted by the miRNA *i*.

The result of this test is displayed in Figure E.18.

We have intriguing results on the human dataset. There are 30 motifs (respectively one, twelve, seven, five, and five in Class total and in Classes I to IV) that have a perfect F-score of 1.0, which is more than the 21 on Seq<sub>W<sub>i</sub></sub>. It is puzzling that we find interaction sites in a noisier environment. However, one possible reason to explain this behaviour could be related to the way how FIMO computes the p-value according to the input sequences. As we only take the best occurrence for each sequence, changing the p-value of some occurrences might change what are the best occurrences.

In the mouse dataset, the results are less surprising with no perfect score and with a
best F-score of 0.83.

In the end, these results demonstrate a possible, yet difficult usage of this approach
even on complete mRNAs. It is interesting to note that the classification in different classes
to search the initial motif θᵢ is mandatory because motifs θᵢ inferred on all interaction
classes (Class total) have lower results than those inferred from each of the individual
classes.

E.4 Perspectives

The results obtained in this first analysis of the possible usefulness of exploiting intra-
species conservation and the associated over-representation of motifs to infer new inter-
<figure><img src="image_85.png" /><figcaption>Figure E.18 – Distribution of the F-score when searching on $Seq_{C_i}$ with motifs $\theta_i$.</figcaption></figure>

action sites and thus also new miRNAs show some promise. However, there is still a lot of work to be done in order to be able to benefit from such feature. It is also obvious that this feature will have to be used together with others.

The global and deterministic approach of SMILE could also be useful to infer motifs composed of more than one box that could target specific classes of interaction sites, but this would require also that we improve the results provided by SMILE through a clustering of the motifs found, especially when substitutions are allowed.

There are several other paths that we could explore. One would be to limit the input of any motif inference method to only the highly accessible mRNA regions, thereby potentially reducing the amount of noise.

Although we put it aside for now, it might be interesting also to study under-represented motifs as inferred by SMILE.

Finally, since we developed a process to validate the fitness of a motif with an interaction, it might be interesting to use this process with classical machine learning algorithms such as genetic ones to obtain the optimal parameters that would ideally retrieve only motifs that are specific to this interaction, if these exist.
<figure><img src="image_86.png" /></figure>
# Contribution: Web-service for finding motifs

The main purpose of this chapter is to give a second life to the motif inference method SMILE [Sagot, 1998; Marsan and Sagot, 2000] previously developed in the team. Although it is quite old, it has features that current methods of motif inference, such as those from the MEME SUITE, handle in a different way or lack altogether. These include, for instance, allowing to impose some constraints on the alphabet of the motifs, or to look for motifs composed of several boxes. Many such methods are furthermore heuristics – this is in particular the case of the methods from the MEME SUIT – while SMILE is an exact algorithm.

Given the vast diversity of the persons working in computational biology, a tool has to be accessible to someone not used to command lines. The best solution for this is to make it available on a website. We started with a desktop application based on ELECTRON. ELECTRON is a framework for building cross-platform (Mac, Linux, Windows) using web codes such as HTML, CSS and JavaScript. From this desktop application, we migrated to a website which is now running and available at http://134.214.213.44/. However, it is in a prototype stage that still requires work together with the implementation of new features in order to be made fully public. This website is presently contained in a virtual machine hosted on the *Cloud girofle* cluster inside the LBBE laboratory.

The whole development of this web-service was pursued with the idea of easy scalability, low maintenance and simplicity of usage for users from both the biological and computer science communities.

We divided this chapter in two sections that are related to the two traditional Methods and Results sections of a paper. The first one briefly explains the framework and organisation of this application. The second section presents its current functionalities and usages.

## F.1 Web-service framework

In this section, we will rapidly present the global framework that supports this application.

There are four parts that compose such framework, they are schematised in Figure F.1. These parts are:

- The client wants to analyse sequences and then access the results in a reasonable
<figure><img src="image_87.png" /><figcaption>Figure F.1 – Diagram of the framework of the Web-service.</figcaption></figure>

time. He/she might also want to share the results obtained.

- The Database MONGODB stores the job parameters and job results.

- The queue from the API BULL based on REDIS works as a buffer that stores uncompleted jobs until a computation worker is available to process the job.

- The NODEJS Cluster contains several workers split in two types: web-server route workers and computation workers. The web-server route workers handle the communication with the client by taking requests such as parameters and input sequences and delivering the results of the job. The computation workers are the task force of the whole application. They handle the CPU intensive calculus and are divided in functions called processors which compute a single task, for instance the motif inference by SMILE. The separation between Web and CPU workers prevents slowness and stuttering of the application responsiveness. Both worker types interact with the database and the Bull queue. The web-server route workers feed jobs to the queue whereas the computation workers eat the jobs from the queue.

The choice for the server environment was NODEJS. There are several reasons that support this decision. One of them is that the initial iteration of this project was the desktop application with ELECTRON which is built on top of NODEJS to provide access, among other things, to the file system. NODEJS is also very popular and used by e.g. Netflix, LinkedIn, IBM...

# i Virtual machine and dockers

A virtual machine, or VM for short, is the emulation of a computer inside another computer. It enables a strong division of several standalone instances for security purposes and convenience. Each instance of a VM can have its own operating system and therefore prevent minor and even major bugs or malicious behaviour to affect other VMs. To reduce
the hardware cost and for the other advantages mentioned previously, VMs are frequently
used on a big computer called cluster. The current virtual machine hosted on the cluster
has the technical specificity of being a decent private computer with 16GB of RAM, 8
CPU cores and 100 GB of memory. However, all the blocks of this application can easily
be upgraded with more processing power or with cores to allow parallel computation by
adding more workers.

In order to facilitate a possible future migration, we ensured a minimal required pack-
age list for this application to work on a virtual machine. The required packages are:

- Fail2ban: a package that prevents malicious persons to brute force the ssh connection.

- Nginx: a package that serves as a reverse proxy and links the local ip of the application with the public ip.

- Python3: Python is required to simplify usage of current and future analytic scripts.

- INTARNA: this is a miRNA-mRNA interaction predictor which can also serve to align two RNA sequences.

- Docker and Docker-compose: These two packages ensure that dockers can run and that different docker modules can communicate.

Notice that DOCKER is an open source platform that helps developers to package, deploy and run applications in containers. These containers embody everything that is needed for the execution of an application, and can consequently be executed on any machine with DOCKER installed. They are basically minimalist VMs with comparable advantages of security, scalability, consistency, isolation and efficiency.

Our whole application is inserted in 3 different DOCKER containers which consist of
NODEJS, REDIS and MONGODB. They communicate together via DOCKER-COMPOSE
which is a tool that allows the cooperation of several DOCKERS by launching or closing
all DOCKERS at the same time with a command.

ii Storage implementation with MONGODB and REDIS

There are two types of memory:

- Volatile Memory which is a temporary memory that will be emptied when the machine is powered off. Random Access Memory (RAM) is the common volatile memory. Despite being expensive, RAM is very time efficient in writing and reading.

- Non volatile Memory which is a long term memory and retains the data even when the machine is powered off. Hard drives and SSDs are the common non volatile memories. They are cheaper, yet slower in terms of reading and writing time than RAM.

Databases can use one type of memory or both, depending on their use-case. For
instance, MONGODB uses only non volatile memory and is aimed for long term persistence
with an implementation of various mechanisms that improve the reading and writing delay.
REDIS, on the contrary, uses both types of memory and aims to store hot-data (data that
needs frequent or fast access). REDIS mainly stores data in RAM, however there are
mechanisms that ensure the backup of the data in case of power shortage. These backup
data have to be written on non volatile memory in order to survive a power shortage.
## Queue storage organisation

The finite amount of computing power available in our application and the possible high number of requests from clients coerce a prioritisation in the order in which the requests are processed. A simple and fair method of the first-in, first-out approach implemented by a queue is a way to address this.

The chosen implementation of the queue is managed by an API called `BULL https://github.com/OptimalBits/bull` which stores data in a `REDIS` database as the queue. `BULL` ensures the atomicity of the queue movement. In other words, `BULL` guarantees that once a transaction of the queue is started, it will end and update accordingly the database. This atomicity prevents the partial update of the database which can produce jobs stuck inside the queue, or even worse with jobs stuck in a computing phase which take slots that would be available for other jobs.

While the job is being computed, the results are parsed and stored in `mongoDB`.

## Persistence storage organisation

Despite the persistent memory, `MONGODB` is very efficient for searching inside a large volume of data. This is very useful in our case, for instance for server-side tables that display only 10, 50, 100... motifs instead of all the 200,000 in a single time.

We opted for the implementation of `MONGODB` due to its free open-source nature and the fact that it is one of the most popular database tools available.

`MONGODB` is a document-oriented database which means that `MONGODB` uses a hierarchical structure that can be compared to the organisation of an XML or JSON. There are three kinds of structures inside a `MONGODB` instance that can be created to organise and store different data types: the structure *database* contains one or several *collections* which contain one or several *items*.

We organised our instance of `MONGODB` `DOCKER` with two databases.

Database-1 stores small organisational data that are mainly useful for the correct execution of the application; we call this type "job metadata". Database-1 contains one collection with several items which are the job metadata. An example of metadata is shown in Listing F.1.

Listing F.1 – Item of the database-1 that contains jobs-metadata as saved in `MONGODB`

```json
{
  _id: ObjectId("6419cd90eb0f39a41a42698f"),
  param: {
    origin_input_fasta_name: 'extendedBothClassIII.fa',
    cl_input_file: 'server_tmp_file/1b2b9cf889ebc94631a258a24cf98531',
    status: 'S-Parced',
    alphabet_symb: [ 'A', 'C', 'G', 'T' ],
    alphabet: 'server_tmp_file/5acfe60eb727945f53beb1a99f54aa93f97946c9',
    email: 'nicolas.homberg@inria.fr',
    custom_name: 'III',
    quorum: '0.06347594261774787',
    boxes: '2',
    b1_min_l: '4',
```
b1_max_l: '5',
b1_sub: '1',
b1_min_spa_len: '10',
b1_max_spa_len: '10',
b2_min_l: '3',
b2_max_l: '4',
b2_sub: '1',
len_min: '7',
len_max: '9',
substitution: '1',
task_id: '41'
},
lastupdate: ISODate("2023-03-21T15:54:37.846Z")
}

The job ID (as `_id`) is provided by `MONGODB` when inserting the new job and is then used during all the process to identify this job. This ID is also carried over to the URL of the results page. Parameters (denoted by `param`) contain the parameters of `SMILE` that are adapted for the usage of the application.

It contains the information about:

- `origin_input_fasta_name` which is the original name of the input fasta file as given by the client. This information is helpful in order to give it back to the client as a reminder of his input file. The key `custom_name` serves a similar purpose and allows the user to name its job. The fasta input file name has to be changed internally because two clients could have the same filename with different sequences (for instance, `'test.fa'`). At the reception of the client input fasta file and to prevent redundancy of several identical fasta files, we compute the `md5checksum` of the file and compare it with all currently saved files. If this file already exists, we use it directly and if it is not present, we save this file with its `md5checksum` result as the name. This file path is stored in the `cl_input_file` key where `cl` stands for "command line." Using the `md5checksum` results as the name allows for a fast comparison with all existing files without having to recompute the `md5checksum` for them.

- `Status` which is an internal code for the job state. `Status` can contain the following code:

- `created` indicates that only the input fasta file was given by the user. Indeed, the fasta file is sent once it is filled-in by the user whereas all other parameters are only sent when the user clicks the "Run smile" or "Run smile + validation" buttons. This behaviour allows to secretly compute the `md5checksum` and ensures the ability to read potentially huge fasta files.

- `Pending` indicates that this job is waiting in the queue.

- `Computing` indicates that this job is being computed.

- `S-Parced` indicates that the results of `SMILE` are being computed and stored in the database.

- `V-Parced` indicates that the results of the validation of `SMILE` are being computed and stored in the database.
- `alphabet_symb` which indicates the letters of the alphabet to be used. It is needed for custom alphabets provided by the client. SMILE reads the alphabet as a file, therefore, these letters are written in a file if it does not exist already and the path is saved in the key `alphabet`.

- `email` which is a key that serves to store the email address and then to send an email when the job is completed.

- `task_id` which is provided by the `BULL` queue when a job enters the state 'computing'. It allows the retrieval of the job progress while it is being computed by the computation worker processors.

- `lastupdate` which contains the date of the last update of this item in the database, for instance when the job-status changes. It is essential to keep track of the age of the data so that we can delete old ones frequently. The deletion of items based on a date is a feature of `MONGODB`. We set the lifetime of an item to one week. We added more layers of data cleaning by setting a process similar to `CRON-TAB` that, every day at 2a.m., automatically iterates over all existing files ( fasta files and alphabet files) and deletes them if they are not used in any item of `Database-1`. The other keys store the parameters of SMILE.

`Database-2` on the other hand stores the result data useful for the client and these results can rapidly reach an important size. It is organised in a collection per job named after the job ID with each collection containing motifs stored as items. An example of a job collection data is shown in Listing F.2.

<caption>Listing F.2 – SMILE results data as saved in `MONGODB`</caption>

```json
{
    _id: 0,
    motif: 'AAAA',
    seq: 2,
    occ: 3,
    seq2pos: { '0': [ 1, 0 ], '1': [ 0 ] },
    Z_1occ: 0.99,
    Z_All: 0.99,
    p_1occ: 0.16108706105163917,
    p_All: 0.16108706105163917
},
{
    _id: 1,
    motif: 'AAAT',
    seq: 1,
    occ: 1,
    seq2pos: { '1': [ 1 ] },
    Z_1occ: 0.99,
    Z_All: 0.99,
    p_1occ: 0.16108706105163917,
    p_All: 0.16108706105163917
}
```
]

For each motif, we store an ID that corresponds to the order in which the motif is parsed. We have then the number of sequences and occurrences stored in the keys *seq* and *occ*. *seq2pos* is a dictionary that stores the position of the occurrences in the sequences. This dictionary alone rapidly contains thousands of occurrence positions spread over several hundreds of sequences. This is for only one motif whereas we may have thousands of motifs. Both the number of occurrences per motif and the number of motifs accumulated can produce a collection having a size close to 1 GB for just one one motif inference job. *Z_1occ*, *Z_All*, *p_1occ* and *p_All* store the Z-score and p-value computed for, respectively, only one occurrence per sequence and for all occurrences.

### iii Modifications done to SMILE

SMILE is the main pillar of this application and is composed of several modules:

- *p_bloc* with the executable name *x-smile* which finds motifs using a suffix tree.

- *Sigstat* with the executable name *e-smile_shuffling* or *e-smile_Ushuffling*. Both take as input the motifs found by *x-smile* and evaluate if a motif is significant by comparing its number of occurrences with the number of occurrences of the motif in sets of sequences obtained by shuffling the input ones.

We brought some modifications to those modules. We transformed *e-smile_shuffling* into *e-smile_Ushuffling* by changing the shuffler used in the 1992 version of SMILE to the faster and more recent U-shuffle [Jiang et al., 2008], notably used in the MEME SUITE. We also added another implementation that helps dealing with the massive amount of data which is the modification of the output of *x-smile* from a file output to a stream into the standard output (stdout). We simultaneously changed the input of *e-smile_Ushuffling* to read from the standard input (stdin). With these modifications, we can pipe the output of *x-smile* directly to *e-smile_Ushuffling* and remove the slow process of writing and reading several times from a file. In addition, between *x-smile* and *e-smile_Ushuffling*, we can add an intermediate process that will simultaneously parse the data of *x-smile* while transmitting these to *e-smile_Ushuffling*. The parser sends the data processed to the MONGODB Database-2 in a bulk of 1000 motifs for better performance.

We also modified the output of *e-smile_Ushuffling* to be a stream. The small gain in performance is due to the same reasons as for the output modification of *x-smile*.

### iv Graphical optimisation

The front-end of an application is everything that is sent to and interacts with the client. It is opposed to the back-end that composes everything that is server-related. The front-end code has to endorse the responsibility of dealing with a vast range of possible hardware and software. Indeed, despite screen sizes ranging from a phone to an ultra-wide screen, the front-end implementation should be comfortable to wield and arrange the layout of the screen component accordingly. Additionally, as the different web browsers such as Safari, Google Chrome, Opera, are not always compatible with all features, we implemented a popular framework called BOOTSTRAP which provides a template for customisable components such as navigation menus, buttons and typography that adapt to the screen size and to various devices.
<figure><img src="image_88.png" /><figcaption>Figure F.2 – Home page</figcaption></figure>

We decided that the best way to display the SMILE data and results is with tables and plots. The table is created with the help of the package *data-table* which requires J-QUERY. *Data-table* allows the user to sort, select and even to delete data in the table and enable a server-side table. A server-side table makes use of a database hosted on the server side. This prevents the whole table from being sent to the user. It is the server that retrieves part of the wanted data and sends it to the front-end code that will enable the rendering of the table.

## F.2 Usage of the web-service

In this section, we demonstrate the current state and features of the web-service. It is in a functional state, yet can still be vastly improved in many ways.

There are currently only two pages or states for this web-service, the main page or home page permits the input of the parameters and launches a job which executes SMILE alone or SMILE followed by a step of validation of the motifs inferred. Once a job is launched, the web-service redirects the user to the results page.

We present the home page and the range of the possible parameters in the first section and then present the features of the results page in the second section below.
i Input of the parameters of SMILE

The home page of the web-service is displayed in figure F.2. It currently contains a small description of the tool SMILE that will have to be updated. There are two menus, one on the side and one at the top. Both menu items vary depending on the home page or the result page. The side-menu which can be hidden by pressing the blue Menu button or if the size of the screen is too small, contains three elements: 1. *Create a new Job*, 2. *Quick fill for a test* and 3. *Recent jobs*.

*Recent jobs* (see Figure F.2) store all the previous results page launched by the user. They are named after the custom name given by the user with the hour and date of creation. If there is no custom name provided, the jobs are named with either *Smile job* or *Smile&validation job*.

Both *Create a new Job* and *Quick fill for a test* redirect to the parameters view, also accessible with the top menus. *Quick fill for a test* will additionally fill out in advance some parameters.

The parameters page is displayed in Figure F.3. Each input field is followed by information tags, which provide a succinct help and description of the parameter. Only the first two fields are optional. They provide the possibility to name the job and to be alerted by email once the job data are available.

The next box corresponds to the input fasta file. Once this field is filled-in, it returns some basic information such as the number of sequences and their average lengths (see Figure F.4). This feedback allows the user to make sure that the web site can read the input file. The number of sequences is further used in the field quorum for the conversion between percentage and number of sequences.

The alphabet field currently proposes, in a drop-down, a list of three choices of alphabets, among DNA, RNA and custom. The "Custom" option allows the user to enter another alphabet by separating each letter by a space or a new line. Additionally, more than one letter may be considered at a given position. For instance, a user can enter *AC* that is interpreted as either *A* or *C* at that position, resulting in *TAG* and *TCG* being considered as the same motif. Furthermore a joker letter *\** is available. The latter only makes sense when used conjointly with a constraint on the maximum number of such jokers.

The frame entitled *General motif information* and the following *Multiple boxes search* (see Figure F.5) set the constraints for the motifs. *Multiple boxes search* can be checked, which creates another field asking for the number of boxes, which will then create for each box, fields for the minimum and maximum length of each box, the space between each box and the number of substitutions allowed in each box. The three fields: *Total motif minimum length*, *Total motif maximum length*, and *Total substitutions* set the general minimum and maximum length and number of substitutions for the motifs including all boxes. If we have three boxes each having one substitution allowed and we set a general substitution of one in *Total substitutions*, the resulting motif will only contain one substitution in either of the three boxes. In a similar manner, we can set a composition constrain for the general motif or for each box individually. When clicking the blue button "add composition", it creates three inline components below. The first field is a drop-down menu which lets the user select one of the letters of the alphabet, the second asks for the maximum number of times of the selected letter in the previous field, and the third component is a button that removes this line.
<figure><img src="image_89.png" /><figcaption>Figure F.3 – Input parameters page</figcaption></figure>
Select a fasta-file to upload ①

<figure><img src="image_90.png" /></figure>

number of sequences : 6 average length = 2867

Select an Alphabet file to upload ①

Select an alphabet

custom

<figure><img src="image_91.png" /></figure>

enter here the alphabet with each symbole separate with a space or new line ex: A T C G TA * (for joker)

<figure><img src="image_92.png" /></figure>

Quorum ①

quorum in pourcentage

33

OR

quorum in number of sequences

1,98

Figure F.4 – Input fasta files, custom alphabet and quorum conversion between number of sequences and percentage.

General motif information ①

Total motif minimum length

5

Total motif maximum length

10

Total substitutions

1

Add General composition constraints ①

<figure><img src="image_93.png" /></figure>

<figure><img src="image_94.png" /></figure>

1

<figure><img src="image_95.png" /></figure>

GC

<figure><img src="image_96.png" /></figure>

1

<figure><img src="image_97.png" /></figure>

Multiple boxes search ? ①

2

Box 1

min length for box 1

2

max length for box 1

3

substitutions for box 1

4

min spacer after box 1

6

max spacer after box 1

1

Add composition constraints for Box 1 ①

T

<figure><img src="image_98.png" /></figure>

1

<figure><img src="image_99.png" /></figure>

Box 2

min length for box 2

3

max length for box 2

7

substitutions for box 2

0

Add composition constraints for Box 2 ①

A

<figure><img src="image_100.png" /></figure>

3

<figure><img src="image_101.png" /></figure>

Figure F.5 – Constraint parameters. In this example, we have defined a custom alphabet of A, T,GC and *
Finally, the last two fields take the number of times the input data are shuffled and the size *k* of the *k*-mers whose number should be conserved during the shuffling. Once the job is launched, the user is redirected to the results page.

## ii Presentation and plots of the results

The result page shows first the job information (displayed in Figure F.6) which covers two aspects. The first is the status of the job and the time remaining for each step of the computation, while the second recalls the parameters which were entered for this job. The status can display 4 different statuses: 'Pending', 'Computing', 'S-parsed' and 'V-parsed' where S and V stand for respectively SMILE and validation.

Once the result of SMILE is available, a new top menu 'Result' is created and the user is further redirected to the results (Figure F.7, even if the validation is still in the process of being computed. The result table is then updated with the validation results once these are available.

The side menu allows to come back to the parameterisation page with *Create a new Job* and *Create a new job with the same parameters*. The latter will pre-fill each with the same parameters of this job. There is also the choice to delete this job with *Delete this job* and to export the results of the job. We separated the export of the placement of all occurrences with their sequences ('Seq2pos' in the database-2 F.2) and the main results of the motifs with their number of occurrences, sequences, Z-score and p-value, presented in the table. *export JSON of each motif-ID to sequences-ID/position* corresponds to the latter and *export to csv* corresponds to the former. Both exports are compressed in Gzip.

There is, for now, only one plot which is created with the library PLOTLY (see Figure F.8) and displayed in an interactive histogram that shows, for each nucleotide of the input sequences, how many motifs were found on each position of the sequence. For instance, a motif of length 7 found in a sequence that covers the nucleotides 10-16 will be counted at position 10. A motif can be found several times in the same position but in different sequences. For instance, a motif *m* can be found at the positions 100-106 in sequence 1 and also at the positions 100-106 in sequence 2. There are two possible visualisations. The first one is to count a unique motif at each position so that the histogram would count 1 for motif *m* at position 100. The second visualisation is to count the number of sequences that are occurrences of a valid motif at a given position; this would give us a count of 2 for motif *m* at position 100. This visualisation is most useful with sequences of similar width. Additionally, we can select rows of the table manually or all motifs with a p-value smaller than 0.05 with a click and only display in the histogram those selected motifs.

Another feature that is partially implemented is the possibility to compute an alignment with a set of sequences provided in a fasta file. For now, it is only able to align the motifs found with the file of the sequences of miRNAs used in the human CLASH data C.2 and to search with INTARNA for perfect complementary matches. However, this needed feature requires additional work such as a better parameterisation and also a placement in a separate queue, instead of the web server worker as is the case for now.
<figure><img src="image_102.png" /><figcaption>Figure F.6 – The result page with the job information and time estimation.</figcaption></figure>
<figure><img src="image_103.png" /><figcaption>Figure F.7 – Result table.</figcaption></figure>

<figure><img src="image_104.png" /><figcaption>Figure F.8 – Example of the interactive histogram implemented.</figcaption></figure>
## F.3 Perspectives

Since the beginning, we have undeniably improved our knowledge on web development and with hindsight, we would have changed the initial choice of some environment packages such as `NOJEJS` and `BOOTSTRAP`.

Indeed, `NOJEJS` is a leader in server code for fast and scalable applications. However, its features of real-time updates and notification are underused in our project. Moreover, it is not the easiest to learn and to maintain. This is especially the case for people who are more comfortable and used to the Python language instead of JavaScript. `DJANGO` or `FLASK`, both server-oriented Python environments, would have made a better starting point and would have helped the future development and maintenance of `SMILE`.

A renovation of the whole design of the Web site would help make it more user-friendly.

We have plans to implement new functionalities such as:

- Cluster the motifs found by `SMILE`. One easy model would be to merge the shorter motifs that are included in a longer one in the case of a search for motifs of different lengths. Another idea would be to search for a motif of maximum length still verifying the other parameters which could be done in an efficient approach when reading the suffix tree used by `SMILE`. A maximum motif, or maxmotif as define in the implementation [Arimura and Uno, 2007] is the longest recurring motif in a sequence, the implementation of Arimura support a certain amount of joker as letter which can be any letter from the alphabet.

We could, furthermore, provide a probabilistic visualisation of the motifs in the same way as `MEME`.

- Provide a visualisation of a selected motif placement on each mRNA. This is already partially feasible with the current histogram implementation. However, we only have information from all input sequences and we lack the possible knowledge of individual sequence. In other terms, there is never the option to select some sequences and see which motifs are inferred on them.

- Implement all features of `SMILE` such as validation on a user-provided sequence instead of the shuffled sequences and the Delta parameter which computes for motifs with several boxes the best space gap between the boxes.

Besides the above, we could also implement the recent and promising post-inference selection method `SEISM` recently developed by a PHD student of the team, Antoine Villié.

Additionally, there is a list of minor quality-of-life improvements that should be fast to implement:

- The options *Quick fill* and *Create a new job with the same parameters* should respectively use a test file and re-use the same file entered by the user.

- Add more options in the drop-down list for alphabets such as amino acids to infer motifs from proteins.

- Add more filters to select the motif. For instance, we could have a two-point slider which would set the minimum and maximum Z-score for either a band-pass or notch/band-reject filter.

- The security of this application could easily be improved by using the HTTPS protocol instead of HTTP.
<figure><img src="image_105.png" /></figure>
Conclusion and Perspectives

In the introduction of this thesis, we outlined the current biological mechanisms in-
volved in the interaction of a miRNA with an mRNA, and introduced the fundamental
features used by the computational approaches for identifying the sites of such interac-
tions, knowing that for some features, there is no current consensus on which approach is
the most appropriate. In the case of some features, there is not even a consensus whether
it should be considered.

In Chapter D, we aimed to investigate two key features in miRNA-mRNA interac-
tions: accessibility of mRNA sites and the seed region. The former is a feature that is
among those debated among the scientific community, while the latter is widely acknowl-
edged as the most important feature in such interactions. All our research focuses on
the mechanisms underlying miRNA-mRNA interactions, without specifically considering
the functional consequences of these interactions. Our research showed interesting results
where accessibility appears clearly observed on average in the interaction sites, regardless
of the interaction pattern or region. However, the accessibility of individual mRNA sites
is on the other not clearly observable.

Regarding the seed, it is surprising to see that some methods predict the interac-
tion better without taking it into account. However, this may be because we are only
considering the interaction mechanism and not the functional outcome of these sites.

In chapter E, we investigated the concept of intra-conservation, with a focus once again
on the underlying mechanisms of miRNA-mRNA interactions rather than considering the
functional consequences of these interactions. Our analysis revealed many identical se-
quences located throughout the mRNAs. However, we were able to extract specific motifs
that resemble the interaction base-pairing patterns, and thus to identify some interaction
sites in complete mRNAs. Further analyses are however required. For instance, we could
test the compatibility of the motifs inferred in one species with those inferred in another
species, thereby considering both intra and inter-species conservation, thereby selecting
only the regions that are conserved in both cases which might enable to reduce the noise.

In chapter F, we presented an ongoing work of a web service that allows for the
inference of motifs using SMILE. Although it is currently functional and available online,
it still lacks additional features that would make it a more user-friendly and useful tool. In
the future, we plan to integrate the two main research directions of this thesis by enabling
the inference of motifs only in the mRNA regions identified as accessible.
<figure><img src="image_106.png" /></figure>
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**Abstract:**

MicroRNAs (miRNAs) are non-coding RNAs present in eukaryotes that regulate the expression of messenger RNAs (mRNAs) up or down. These miRNAs have significant potential in future treatment of cancer and other diseases. The miRNA-mRNA interactions are intricate and involve various mechanisms, such as sequence complementarity, accessibility, and conservation. This thesis focuses on two such mechanisms, namely accessibility and intra-species conservation of the site of interaction, using experimental data from Cross-linking, Ligation And Sequencing of Hybrids (CLASH). Although the accessibility of interaction sites on mRNAs is generally observed, it is not consistent for all interactions. Intra-species conservation is a rare feature, which we explore by inferring conserved motifs from mRNA interaction sites. Although the results are noisy, in some specific cases, we manage to retrieve some mRNA interaction sites from the inferred motifs.

**Résumé :**

Les microARNs (miARNs) sont de petit ARN non codant présents dans tous les eu-
caryotes qui régulent, positivement ou négativement, l'expression des ARN messagers
(ARNms). Les miARNs ont un potentiel important pour de futurs traitements du cancer
et d'autres maladies. Les interactions miARN-ARNm dépendent d'une variété de méca-
nismes complexes, tels que la complémentarité des séquences, l'accessibilité et la conser-
vation. Cette thèse se concentre sur deux de ces mécanismes, à savoir l'accessibilité et la
conservation intra-espèce du site d'interaction, en utilisant des données expérimentales de
Cross-linking, Ligation And Sequencing of Hybrids (CLASH). Bien que l'accessibilité des
sites d'interaction sur les ARNms soit généralement observée, cela n'est pas le cas pour
toutes les interactions. La conservation intra-espèce est un mécanisme peu considéré que
nous avons étudiée au travers la recherche de motifs conservés dans les ARNms. Bien que
les résultats obtenus soient bruités, il est possible de retrouver via ces motifs certains sites
d'interaction sur les ARNms.