 ¡Hola everyone! Mi nombre es Cristóbal Gallardo y voy a introducirles en la escena de género de género. Las dos principales preguntas son de orientación o tutorial. La primera es ¿Cómo puede que la género se asamble contra un agujero de géneros contaminados de otros géneros? La segunda es ¿Cómo puede que la secuencia de datos sea utilizada para obtener un género de asamble? En este tutorial vamos a adrecer cinco principales objetivos. La primera es saber las características básicas de SARS-CoV-2. La segunda es entender la nanopore y la iluminación de las tecnologías de secuencia. La tercera objetivo es detectar y retirar la género de género de SARS-CoV-2. La cuarta objetivo es entender el ámbito de la género de género de género y la última objetivo es producir y hidrar la género de género. Este es el workflow que vamos a seguir en esta práctica. De primera forma, introduciré algunas teorías básicas sobre SARS-CoV-2 y nanopore y iluminación de tecnologías. Luego voy a mostrar cómo obtener los datos de un database público en el CPI. Después, performaremos el acertamiento de datos de calidad. La siguiente tip será detectar y retirar las raíces humanas de la secuencia de SARS-CoV-2. Y el paso de la lab será proporcionar la asamblea de género. SARS-CoV-2 virus es un virus veta-coronavirus que corresponde a la familia corrobiridae. El género es un símbolo positivo en la RNA de 30 kilómetros. Incluye 14 funcionales que encoden 9,860 aminoácidos. Coden 4 proteínas estructurales y 23 no estructurales. Las proteínas estructurales están codificadas por los géneros SPIKE, ENVOLOPE, MEMBREN y NUCLOC-ABSIT. NSP géneros encoden la complexa de TARCIP-DASE. Como podemos ver, los proteínas SPIKE, MEMBREN y los proteínas ENVOLOPE son un rango en la superficie. Los proteínas SPIKE son plico proteínas con un rango de tropización para el mamaleón ACE2 receptor, que considera el target de la tarjeta primaria en la que se asocian los virus. Para esta práctica, usaremos una combinación de lo largo y lo largo para producir el género. El lo largo será usado para obtener un asamble inicial. Luego, los lo largo serán usados para resolver las ambigüidades potenciales en el género anterior. Finalmente, los lo largo serán usados para producir el asamble para remover los potenciales. Los lo largo serán generados por la tecnología ilumina, caracterizados por el lo largo y lo largo. En el otro lado, los lo largo serán generados por la tecnología OXFORN, caracterizados por el lo largo y el lo largo. El proceso de ilumina básica de la secuencia envolve seis principales. En el primer paso, después de fragmentar la DNA a varias piezas, los adaptantes están editados. Cada nanófono contiene oligolucrotides que proporcionan puntos de encorreamiento para los adaptantes. Luego, los fragmentos núcleos vencen y se atacan a los oligolucrotides. En el primer paso, se atacan a la secuencia primaria, que es reconocida por la polimería de la DNA. Durante la polimerización, recientemente, los DnTPs están usados para sintetizar el tránsito complementario. Cada dos bases emitirán un ojo en una lenta unica, que permite identificar la base que está incorporada. En el segundo paso, después de la polimerización, el DnTP es naturalizado. El proceso de secuencia es repetido sobre y sobre otra vez, así que todos los DnTPs en una área proceden de una sola base, un proceso llamado la amplificación clonal. La tecnología de la nanoporca de oxígeno funciona en un modo muy diferente. En este caso, los ácidos de ácidos núcleos pasan por una nanoporca y se cambian en el ácido eléctrico. La magnitud de la intensidad actual depende de la composición de los ácidos núcleos que ocupan la nanoporca. Finalmente, esta información es usada para identificar la secuencia de los ácidos núcleos. Para este tutorial, vamos a usar un total de seis diferentes samples, todos de los que están publicamente accesibles a través de la plataforma NCBA. Ahora podemos empezar con nuestra analisis. El primer paso es crear una nueva historia y darse un nombre correcto. Por ejemplo, SARS-CoV-2 Asamble. Ahora debemos crear dos nuevos datos, listados en los números de accesión de nanoporca ilumina que vamos a usar para formar el asamble de oxígeno. Se debemos abrir el asamble de oxígeno, pese los números de accesión, mantiene un nombre correcto, y decir tabular asType. Entonces, comencemos. Ahora debemos hacer exactamente lo mismo, pero con los números de accesión de nanoporca. Se debemos renunciar a las accesiones de nanoporca y darse un tabular asType. Entonces comencemos. Es recomendado para agarrar nuestros datos, porque es posible agarrar los dos branches de nuestra analisis. Abar el dato, empujar el edificador de datos, y añadir el asamble de nanoporca. El siguiente paso es retirar los datos desde el database de NCBI, usando el tabular asType y extras de accesión en el formato de fastQformat. Seleccione el tabular asType, el listado de accesión de sRi, y ejecuta. Finalmente, cuando la data de nanoporca only contains single n reads and the Illumina data only contains pyrn reads, we can remove the empty files from the history panel. In order to do that, we should activate the operation of multiply data sets. Select the empty files, delete the data sets. Perfect. And now we can rename the data sets. Nanopor reads and Illumina reads. Quality control, read streaming, and filtering are essential pre-processing steps required to guarantee accurate results from our RNA-seq analysis. Due to the very different nature, Illumina and Nanopor reads should be processed by using different tools. The Illumina reads will be processed by using the fastP tool, and the result will be analyzed by using the multi-QC tool. On the other hand, the Nanopor reads will be processed by using the Nanoplot tool. Let's go with the quality assignment. We should select the fastP tool, pair collection, the Illumina reads. Now we'll configure the tool to retain reads only if at most 20% of their bases have a thread-scale quality higher than 20. And if delaying the basis after the streaming is at least 50% and execute. This process can take some time. Meanwhile, we can launch the Nanoplot tool. We should look for Nanoplot in the search bar. Select Nanoplot. We should select the correct file. Nanopor reads. And in the option for filtering, we should select logarithm scaling of length in plot and execute. Once the process has finished, we can analyze the results by using the multi-QC tool. Look for it in the search bar. Select fastP as tool. Select the data set. We can give a name to the report and execute. Now we can analyze the result of the quality assignment by using the multi-QC tool. For example, we can see the percentage of reads which has passed the filter. Also, how much are considered at low quality. It also provides information about the duplication rate. The insert site which is around 150 base pairs sequence quality before and after filtering. The one in citizen content. We can see clear differences after processing the reads. After we can have some information about the unknown bases. Now we are going to analyze the report generated by the Nanoplot tool. It provides so much information about our reads such as the mean read length which is around 345 base pairs. The mean read quality which is around 9, much lower than the Illumina quality values. It allows to characterize quite well our data sets. Also, it provides different plots which can be used in our publications. Sign the search scope to samples where obtained it from human thesis is necessary to remove the potential contamination by retaining only those reads that don't map to the human genome. As with quality control differential characteristics of Illumina and Nanoplot reads require to use different tools for mapping the reads. Thus, we'll use both the two for mapping the short reads. This tool is optimized for an error rate typical of the Illumina sequencers and for mapping the long reads we'll use minimap 2. Let's go to map the Illumina reads. We should look for the both the two tool. Now we should select pair and collection select the output of the fast P tool. Now we should select the reference genome for mapping HG30H which is the last revision and save the mapping in the history and finally execute. Now we are going to map the long reads In order to do that we are going to use the minimap 2. We should select the same reference genome single single reads Now we should select the nanopore dataset and also a nanopore read to reference mapping and we can leave the rest of the parameters of default and execute. Once the sequence have been aligned we are going to use the some tools stats in order to generate some statistics from the minimap alignment we should select the one file select separate datasets and summary numbers we can leave the rest of the parameters and execute. Now we are going to compare the statistics generated by both alignments we can open one of the dataset generated by both the two and one of the datasets generated by some tool stats that is now we can compare both it provides a lot of information such as the row total sequence and the read map it we can see most of the reads didn't map which has sense because most of the reads belong to the viral genome well it provides additional information such as the maximum length and if we compare those results with the both the two we can observe a similar trend most of the reads didn't map the next step is to extract the human reads from our sequence in order to do that we are going to use the tool SamToolsView we should select the alignment generated by both the two then in the require this flag we should set read is a map it and made is a map it and we can leave the rest of the parameters as default and execute now we are going to repeat the process but using the reads generated by nanopore in this case we should select read is a map it and leave the rest of the parameters as default and execute before starting the assembly it is necessary to carry out a 3D of transformation on our data to adjust its format firstly we will use the tool fastx tool to extract the sequence in fastq format next we will merge the dataset into single dataset by using the collapse dataset tool finally we will generate a subset of the Illumina reads by using the sectq sample tool once we have removed the contamination from our sequence the next step is to extract the reads in fastq format since this is the format required by the alignment tools we need to select the Illumina dataset compress fastq at format and as output read1 and read2 and execute now we should do exactly the same but with the nanopore dataset in this case we should select and specific as output and execute now we are going to combine the list collection into a single file dataset in order to perform this operation we are going to use the collapse dataset tool now we should select each of the datasets and execute we should repeat the same process execute and the last one done finally the last step before assembly or genome is to don't sample the Illumina sequence this step is optional but if you don't have so much time I recommend you to do it because assembly the full set of sequence can take around 10 hours in order to do that we are going to use the second tq sample we should select the Illumina datasets and execute finally everything is ready for performing the genome assembly it can be defined as a process whose objective is to reconstruct a genome from the reads obtained by sequencing technologies in this practice will perform at the nova genome assembly which refers to sequencing a novel genome where there is no reference sequence available for alignment two common types of the nova assemblers are greedy algorithm assemblers and grab method assemblers greedy algorithm assemblers basically find overlap between reads and then builds a consensus sequence from the aligned overlapping reads it is easy, but however it don't work well for large reads on the other hand grab method assemblers represent reads as a set of nodes an overlap between these reads at the dead edge with counted this node to form a complete grab grab method assemblers are characterized for being computationally more expensive but they perform well or large reads datasets we'll use an assembler to base it on the bridging grabs which is the grab model used by most genome assemblers during the assembly process reads are broken into smaller fragments of a specific size, the cameras which are then used as nodes in the grab assembly the nodes that overlap are then connected by an edge will represent the reads an ideal assembly corresponds to the path that travel to every know exacting ones unicycler a software tool designed specifically for hybrid assembly of smart genomes unicycler employs a multistitch process that utilize a set of software tools spades is used to assemble the Illumina reads into an assembly grab on the other hand nanopores reads are assembled by using a combination of Minium and Raccoon then both assemblies are merged and finally Pilon is used to fix misassemblies let's go to use unicycler then we should look for the tool in the tool search bar we should select the reads the expected number of linear sequence which is one and execute it can take some time once unicycler has finished we are going to use the tool bandage it will allow us to explore the assembly grab through summary reports and visualization of the content we should select the final assembly grab and execute now we are going to generate an assembly grab visualization by using the bandage image tool we should select the final assembly set the correct parameters and execute finally we are going to have a look to the statistics generated by bandage info it provides a lot of information probably the most important one is the largest component in base pair which is quite similar to the spectre size of the virus which is around 30 kilobases and that's all I hope you enjoy