Sequence Assembly
Sequence Assembly - Related Videos
09:34
Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly
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Cited by 2 •
2020
A protocol is presented for the synthesis of information-encoded peptoid oligomers and for the sequence-directed self-assembly of these peptoids into molecular ladders using amines and aldehydes as dynamic covalent reactant pairs and Lewis acidic rare-earth metal triflates as multi-role reagents.
11:36
Assembly, Loading, and Alignment of an Analytical Ultracentrifuge Sample Cell
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Cited by 21 •
2009
The analytical ultracentrifuge (AUC) sample cell holds sample and reference buffer and during experiments and is exposed to high vacuum and rotor speeds up to 60,000 rpm. This video will demonstrate the rigorous attention to detail necessary for assembly, loading and alignment of this very important component of an AUC experiment.
08:57
Amplicon Sequencing using the Long-Read Sequencing Technologies
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2025
This protocol was optimized for targeted deep sequencing of 18 drug-resistance regions in Mycobacterium tuberculosis using a long-read sequencing platform, followed by analysis with a tuberculosis-specific bioinformatics pipeline designed for long-read data.
09:45
Detection of Copy Number Alterations Using Single Cell Sequencing
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Cited by 9 •
2017
Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.
02:41
Protein Complex Assembly
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2020
Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types. Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes. Many viruses self-assemble into a fully functional unit using the infected host cell to...
FAQs
Overlap-based algorithms compare sequencing reads directly, identifying matching sequence regions before merging compatible fragments. De Bruijn graph methods instead represent shared k-mers, or fixed-length subsequences, as connected graph paths. The choice between these approaches affects how available read information is processed and influences the resulting contigs.
Read accuracy determines how confidently algorithms recognize relationships among fragments, while coverage describes how extensively the underlying sequence is represented by reads. Repetitive regions can complicate fragment placement because similar sequence patterns may support more than one connection. Together with the selected algorithm, these factors influence whether the resulting assembly is reliable for downstream analysis.
Contigs are formed when compatible fragments are merged into continuous assembled sequences. Scaffolds extend this organization by using additional linking information, including paired-end or long-read data, to connect contigs. Thus, a scaffold can represent a larger structural arrangement even when the underlying sequence is not continuous across every region.
Validation is essential because an assembly can be affected by read accuracy, coverage, repetitive regions, and algorithm choice. Reviewing assembly quality before downstream analysis helps researchers judge whether the reconstructed sequence is sufficiently reliable for its intended use. This step is especially important when the assembly will support annotation, comparative analysis, or biological interpretation.
A typical workflow begins with sequencing reads or their shared k-mers, followed by computational identification of overlaps or graph connections. Compatible fragments are then merged into contigs. When paired-end or long-read information is available, those contigs may be organized into scaffolds. The resulting assembly is subsequently validated before researchers rely on it for downstream biological analysis.
Sequence assembly supports genome annotation, comparative genomics, and pathogen surveillance by providing longer sequence structures for biological investigation. It is also useful for characterizing organisms that lack suitable reference genomes. In transcriptome studies, assembly helps researchers analyze RNA-derived sequence information when complete transcript sequences are not directly available.