Illumina skin minecraft

Illumina skin minecraft DEFAULT

Chapter Text

What Illumina knows is this: the brain doesn’t work like a camera. Memories aren’t stored with precision or even order.

Caught up in the stress and the exhilaration, the MCC passes by in a blur. All Illumina remembers are brief flashes. It’s like a dream that never was. He remembers dodging arrows. He remembers Sasha hollering in excitement at a kill. He remembers the feeling of his feet giving way into flight, the way Fruit instinctively turned to punch him and smile in Hole in the Wall.

They didn’t win, but it was close. They were all so happy, so excited, and though Illumina would rather play eight rounds of Buildmart with blazes than talk to people during a party, he lets Sasha and Zeuz take him under their wing, for a little while. A buffer between the rest of the loud loud fireworks and people and riot of colours that mix into themselves when he closes his eyes.

Still, that doesn’t stop him from saying ‘Goodnight’ after twenty minutes and leaving to jump worlds. He speedruns for a while. It’s relaxing. Chat’s exuberance matches the one he left behind, except he doesn’t have to worry about saying the wrong thing or making eye contact, and he chops wood and swims and parkours until he’s too tired to go on.

Another goodbye, and he’s pulling open the door of the apartment he and Fruit share. They could get separate houses, they’re more than well-equipped to build their own. But Illumina likes staying with Fruit. He’s kind, and smart, and doesn’t do anything more than laugh when Illumina starts rambling about speedrun strategies or cats or whatever comes into his mind. And it’s a fond laugh, too, like he’s laughing with Illumina, not at him.

Basically: they are roommates, and he loves it. That’s it.

Speaking of Fruit, he’s beaten him to the couch, sleeping face down. That can’t be comfortable, but he doesn’t seem to mind. His communicator is on the floor, inches below an outstretched hand. The screen is still lit from unread messages.

Illumina puts his comm on the table and gets him a blanket. When he drapes the blanket over his sleeping form, his fluffy green ears perk up and he trills, deep in his chest. He can’t help but laugh.

What Illumina knows is this: the enchantments that come with being part of a team on MCC give you some of the features of your team mascot. It’s part marketing strategy, part fun. Except it’s just mostly fun, because the MCC is about enjoying yourself and god, he doesn’t know where he was going with that. Everyone just likes dressing up in their team colours and wearing dog ears.

Or, in his case, parrot wings.

Anyway, the enchantments are applied right before the event and fade in a day. There’s no reason for them to last that long. Sure, you can reapply them if you like the look, but why bother? It’s a fun, event-only thing. He likes it that way.

Illumina goes to sleep.

He wakes up with bright pink wings.

##

He doesn’t freak out at first.

(That’s a lie. When he saw the wings in the mirror he’d jumped, screamed, and brought Fruit crashing through the door with a newly crafted axe from the wood of their house.)

...He freaks out a little. Of all the things that constantly happened to him, keeping his wings weren’t on the list, not really. He sends a whisper to Scott while Fruit grumpily repairs the house.

(“Why’d you scream like that? You’ve faced down dragons!”

“Leave me alone! I was half-asleep, okay, I wasn’t expecting to see frickin’ two feet long wings in the mirror!!”

“Well, I’m sorry, I thought you were like, being attacked or something! That scream!”

“Maybe I was! Maybe...maybe my wings were attacking me, you know?”

“Illumina, no.” Fruit says, horrified, but he’s laughing.)

Scott doesn’t reply. Illumina shuffles through his memories for a moment before remembering he must be in the Last Life SMP at the moment. He’ll probably be out of contact for a few weeks, then. Uh oh.

He resolves to give it a while. Maybe it’s just taking its own sweet time. Magic sometimes does that without any reason or rhyme.

He waits. One day passes. Then another.

##

It’s been exactly three days, and Illumina’s restless. Restless, because he’s long since recovered from the strain of MCC, and as far as his hobbies are concerned he’s got nothing to do indoors. He speedruns, and he speedruns, and he speedruns some more. When people ask him how he never gets bored of speedrunning, he stares blankly at them with incomprehension. Speedrunning is all he’s ever loved.

Sometimes, he had felt lonely, being the only creature capable of language in the colourfully barren world. But then he accessed the Hub, and met Fruit, and HBG, and Chat, and now he’s not lonely anymore. If he ever was.

But he is impatient to run, and that leads to him sitting curled up in a chair while Fruit calmly enchants a new weapon, glaring at the wall like it’s going to suddenly drop the current world events. “I don’t - I don’t think it’s that big of a problem.” he says to the wall.

Fruit makes a skeptical noise. “It’s okay if it’s not.” Indecipherable glowing letters flit around him. He carefully scratches a Sharpness rune into the flat of his blade.

“It is!” Illumina insists, already making his way to the door. “I’ll just go speedrun like normal. It’ll be fine.”

He doesn’t even know what held him back in the first place. Fear of looking odd maybe, odder than he already is, dressed in all black with only his eyes visible. Fear of the cosmetic magic going awry while he speedruns, dropping him into lava or off the edge of a bastion at an inconvenient moment. The wings are a little heavy on his back, enough to throw off his center of balance when he spars with Fruit. It wouldn’t happen. But it could.

It could.

##

Barely an hour has passed before the door creaks open. Illumina shuffles through the gap, his posture the dictionary definition of dejection. His wings droop. His shoulders slump. His face is scrunched in a small frown.

He makes a beeline straight to the couch and stares down at its current occupant. Fruit’s been sprawled on the couch ever since he finished enchanting, but he gets the hint and lifts his legs to make room.

Illumina sits. Fruit immediately plops his feet into his lap.

Illumina doesn’t even complain about how gross it is. That’s how he knows something is wrong.

“You okayy?” Fruit mumbles, nudging him.

“...I can’t speedrun like this.” Illumina says eventually. “I don’t want the mods to think I’m cheating.”

Fruit squints hazy eyes at him. “You almost got world record in the Purple Pandas skin.”

“That’s different!” He laments, comically dismayed. “Panda ears don’t help you fly!”

“Pink Parrot wings don’t help you fly, either.”

“They look like they do.” Illumina gives them a flap for emphasis, and he has got to admit he kind of has a point. They’re large, almost as big as his torso, and while Fruit knows they do jack shit unless they’ve got an elytra on - Philza and Grian’s wings were absolutely gigantic for a reason, after all - a case could be made for mitigating fall damage or other unfair advantages.

They’re also kind of fucked up. He stares and what comes out of his mouth is: “What’d you do to them?”

Illumina twists around, stretching his wings out as far as he can. Fruit can see the moment realization dawns on his face. “...So you know how I said I was speedrunning?”

“Yeah?”

“I might’ve fallen in a lava pool. And gotten killed by a Blaze. And an iron golem. It didn’t hurt that long, but I guess my wings got a little ruffled.”

As always, when it came to Illumina, ‘a little ruffled’ was an understatement. Just looking at them made Fruit’s paws itch. He’d groomed his fair share of wings in the past, and he could tell that Illumina’s was as bad as you could get without actually being injured. The feathers were askew, the primaries out of place, and there were bits of mud and grass peppering the wings. He wouldn’t be surprised if he found Endermites in there, too.

“- I mean, I noticed they were pretty scuffed after MCC, but I was kind of thinking they’d go away then,” Illumina’s rambling when he tunes back in, nervously running a hand through his hair. “I wasn’t really thinking about them, I’ve never really had wings before, they’re a little heavier than elytra, and I was pretty tired, and - “

“Illumina.” Fruit says, gently at first, then a little louder. “Illumina. Hey man, it’s okay. Chill out.”

He shuts his mouth with a distinctive snap. He doesn’t blush anymore like he used to. With Fruit, he’s too comfortable for that. He’s seen all the deepest, darkest parts of him and not flinched away. Living together has made them weather some of their most awkward, unfunny moments together. But he does give him a big sad pout.

Fruit exhales in an approximation of a laugh. “Go take a bath,” he says. “I’ll groom your wings afterwards.”

Illumina sits there for a moment. “Okay.” he says. Then, “Your feet are kinda gross.”

Fruit laughs for real. “Yeah, I know.”

Notes:

chapter title inspired by @illumina-but-everywhere's fruitninja tag. praise be to thee for the food

i haven't written fic in 3 years and this is the first thing i actually want to write. i blame fruitninja mcc. anyway umm...tell me your favourite part if you enjoyed my fic? Widepeepohappy i'm at @spookystew on tumblr

Sours: https://archiveofourown.org/works/34030900/chapters/84645544

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Sours: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565314/
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The Minecraft Championship (MCC) is an unofficial Minecraft event held a couple of times a year that pits teams of popular Minecraft streamers against each other in various game mode mods. After taking a short break during the first half of 2021, it seems that they have returned with their monthly schedule.

With MCC 18 right around the corner (Oct. 23, 2021), many of us are interested to know who will compete on each team. As of publishing, the hosts have not announced all the players that will compete, but they have announced (most of) the team names. Refer to the list below to see who is on each team!

Related: All Minecraft Championship Winners in MCC History

  • Red Ravens
    • Wisp (Wispexe)
    • Philza (Ph1LzA)
    • JackManifoldTV (JackManifoldTV)
    • TommyInnit (tommyinnit)
  • Orange Oozes
    • GizzyGazza (GizzyGazza)
    • Mefs (Mefssss)
    • TapL (TapLHarV)
    • Krtzyy (krtzyy)
  • Mustard Mummies
    • Sylvee (sylveemhm)
    • Tubbo (Tubbo, TubboLIVE)
    • sapnap (sapnap)
    • smajor (Smajor1995, Dangthatsalongname)
  • Lime Liches
    • GeeNelly (GeeNelly, GeeNelson)
    • Illumina (IlluminaHD, Illumina1337)
    • CaptainPuffy (CaptainPuffy, CptPuffy)
    • Ryguyrocky (Ryguyrocky)
  • Green Goblins
    • Grian (GrianMC)
    • Nihachu (Nihaachu)
    • HBomb94 (hbomb94)
    • GeminiTay (GeminiTayMC)
  • Cyan Centipedes
  • Aqua Abominations
  • Blue Black Cats (unconfirmed name - likely to change)
  • Violet Vampires
  • Fuchsia Frankensteins

One final note: The official MC Championship Twitter has stated that costumes are mandatory! Expect to see some paranormal Minecraft skins face off when the event is hosted on Oct. 23rd.

For the latest guides, news, and information about all things Minecraft, be sure to check out our Minecraft Homepage!

Follow us on Twitter and Facebook to get updates on your favorite games!

Recently Updated Minecraft Posts

Sours: https://progameguides.com/minecraft/all-minecraft-championship-mcc-18-teams/
Illumina...EWW + Illumina reacts

Descargar Musica Illumina Dominating Tgttosawaf For 7 Minutes Mcc 12 Mcc 13 Mcc P21 Gratis.

illumina destroying tgttosawaf in mcc (minecraft championship)
from mcc 12, mcc 13, and mcc p21
+ bonus punz trying to speedbridge like illumina

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    36,819 192 kbps75.58 MBJoin the admin team as they spectate the fifteenth edition of MC Championship Originally streamed at twitch.tv/thenoxcrew/ on the 24th July 2021. Follow MCC on Twitter: ...DownloadDownload mp3

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MC Championship 15 - Admin POV

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Highly comparable metabarcoding results from MGI-Tech and Illumina sequencing platforms

Introduction

Metabarcoding, the identification of organisms via DNA marker genes from environmental samples or a mixture of heterospecific specimens (Taberlet et al., 2018), is a powerful tool in biodiversity analysis (Kelly et al., 2018; Pont et al., 2021; Valentin et al., 2019; Watts et al., 2019). This approach has been efficiently used to characterize the community composition of microbial and animal taxa from various types of environmental samples such as soil (Bahram et al., 2018; Nilsson et al., 2019), water (Djurhuus et al., 2018; Liu et al., 2020), sediments (Kang et al., 2021; Wurzbacher et al., 2017), dust (de Groot et al., 2021; Rocchi et al., 2017) and feces (Ando et al., 2020; Anslan et al., 2021). In animals, metabarcoding has also been widely used to identify host-associated microbiomes, determine the structure of entire holobionts and dietary differences in various species (Alberdi et al., 2019; Kueneman et al., 2019). The information acquired through DNA marker gene sequencing has greatly boosted our knowledge about the ecology and distribution patterns of various aquatic and terrestrial animal groups such as nematodes, arthropods and annelids (Arribas et al., 2016; Beng & Corlett, 2020; Compson et al., 2020; Deiner et al., 2017; Zawierucha et al., 2021).

Since the mid-2000s, the metabarcoding technique has greatly benefited from technological advances in library preparation, primer and sample-specific index design, novel sequencing platforms as well as from optimized bioinformatics workflows and accumulating reference data (Taberlet et al., 2018; Nilsson et al., 2019). Short-read, second-generation high-throughput sequencing (HTS) technologies are currently the most widely used means for metabarcoding due to a relatively low cost per sample, high sequencing depth and accuracy. Sequencing instruments produced by Illumina, Inc. (e.g., MiSeq and NovaSeq) using sequencing-by-synthesis technology are dominating the market as they offer viable solutions for both ultra-high sequencing depth and paired-end sequencing of short- and mid-sized amplicons (up to 500–600 bases; Kumar, Cowley & Davis, 2019). By utilizing recent advances in DNA nanoball sequencing technology (Drmanac et al., 2010; Li et al., 2019), MGI-Tech, Inc. has produced several DNBSEQ (MGISEQ) platforms with similar throughput and quality profiles compared with Illumina sequencing (Jeon et al., 2021; Kumar, Cowley & Davis, 2019). The results from Illumina and MGI-Tech sequencing platforms are highly comparable and may be used interchangeably for RNA sequencing and whole genome sequencing (Jeon et al., 2019; Kim et al., 2021; Korostin et al., 2020). However, the error rate of DNBSEQ technology (MGI-2000 instrument) was marginally higher than for Illumina (HiSeq instrument) when using 2 × 150 paired-end sequencing mode on both platforms (quality scores >30: 95.03% and 97.18% for MGISEQ-2000 and HiSeq 2500, respectively; Korostin et al., 2020). The results of these early genome sequencing-oriented studies suggest that MGI-Tech platforms may be used efficiently also in metabarcoding studies. In early 2021, sequencing costs for MGI-Tech DNBSEQ-T7 were about 50% lower compared with Illumina NovaSeq platform (cost per read) for the greatest throughput analyses (Tedersoo et al., 2021). So far, only a single metabarcoding study has been conducted to compare these sequencing platforms (DNBSEQ-G400 and Illumina MiSeq) for recovering rRNA gene 16S and ITS amplicons of bacterial and fungal mock communities (Sun et al., 2021). For the ITS2 amplicon, Sun et al. (2021) reported small but significant differences between DNBSEQ-G400 and MiSeq platforms, but this difference can be attributed to use of different primer pairs for DNA library preparation.

Here, we aim to compare the relative performance of DNBSEQ-G400RS (2 × 200 bp) and Illumina NovaSeq 6000 (2 × 250 bp) for DNA metabarcoding. DNA libraries were prepared from the same pools of mitochondrial cytochrome oxidase 1 (COI) amplicons generated from soil DNA extracts.

Methods

Sampling

We selected 120 sites (Table S1) in an area of 500 km2 around Tartu, Estonia, which included various terrestrial ecosystems (managed croplands, abandoned croplands, plantations of fruit trees and forestry trees on former agricultural land and old forests (>80 years)). In each site, we established a sampling plot (30 × 30 m) in a homogeneous patch to minimize any edge effects and vegetation gradient effects. In a 3 × 3 grid, we collected nine soil cores from each plot, with coordinates representing the central location (Table S1). Soil cores for analyses of soil bulk density were collected by hammering a PVC tube (50 mm diam.) to 100 mm depth after removing loose litter. Using a clean knife (sterilized in 1% NaOCl solution), roughly 15 g of soil was scraped from sides of the same core holes and pooled into a clean Zip-Lock plastic bag. The composite sample was well mixed and frozen immediately among freezing tablets (initial temperature, −86 °C). In the laboratory, the frozen samples were transferred to −80 °C.

Molecular analyses

The frozen samples were crushed using a hammer in double plastic bags (to speed up thawing), placed into paper bags and dried in a drying cabinet at 35 °C for 24 h. The dried samples were transferred into Zip-Lock bags, followed by initial homogenisation by vigorous rubbing by hands. Roughly 1 g of homogenised soil dust was transferred into an Eppendorf tube and subjected to further homogenisation by using two 3-mm steel balls at 30 Hz. Altogether 0.25 g of soil powder was subjected to DNA extraction using a Thermo Scientific KingFisher Flex robot and MagAttract PowerSoil kit (Qiagen Inc., Hilden, Germany), following the manufacturer’s instructions.

For amplification via PCR, primers mlCOIintF (5′ GGW ACW GGW TGA ACW GTW TAY CCY CC; Leray et al., 2013) and jgHCO2198 (3′ TAI ACY TCI GGR TGI CCR AAR AAY CA; Geller et al., 2013) were used to target the ~313 bp mitochondrial cytochrome oxidase 1 (COI) gene. The former primer was barcoded with unique phase shift indexes (Table S2). The 25 µl PCR mixture comprised five µl of 5× HOT FIREPol Blend Master Mix (Solis Biodyne, Tartu, Estonia), 0.5 µl of each forward and reverse primer (20 mM), one µl of DNA extract and 18 µl ddH2O. Thermal cycling included an initial denaturation at 95 °C for 15 min; 25 cycles of denaturation for 30 s at 95 °C, annealing for 30 s at 57 °C, elongation for 1 min at 72 °C; final elongation at 72 °C for 10 min and storage at 4 °C. All PCR reactions were performed in duplicate and pooled for subsequent analyses. PCR products (five µl) were verified using 1% agarose gel electrophoresis. Samples yielding no product were re-amplified with 30 cycles, followed by pooling and gel electrophoresis. Both a negative control (ddH2O with no DNA template) and a positive control sample (an artificial DNA molecule with multiple primer sites) were used to assess obvious contamination during sample preparation for PCR and the efficiency of PCR, respectively. The PCR products were normalized for preparation of two libraries based on visual inspection of band strength on an 1% agarose gel. We used the following criteria: no band (i.e., negative control) = 10 µl; faint band = seven µl; medium band = three µl; strong band = one µl. The pooled amplicons were shipped for Illumina NovaSeq 6000 (2 × 250 bp; hereafter NovaSeq) paired-end sequencing in Novogen Inc., UK and for MGI-Tech DNBSEQ-G400RS (2 × 200 bp; hereafter DNBSEQ) paired-end sequencing in Clinomics Inc., South Korea. The service providers were selected strictly based on the best price offer for delivering 50 million reads (Table 1). Sequencing libraries for respective sequencing platforms (including adapter ligation) were prepared by the service providers from the same amplicon pool. NEBNext® Ultra™ II DNA Library Prep Kit (PCR-free workflow) was used for NovaSeq (at Novogene Inc., Cambridge, UK) and MGIEasy FS DNA Library Prep Set (includes PCR step after ligation) was used for DNBSEQ library preparation (at Clinomics Inc., Ulsan, South Korea). The sequencing costs are provided in Table 1.

Table 1:

Cost calculations for Illumina NovaSeq 6000 and MGI-Tech DNBSEQ-G400RS based on the best offering service providers and data retrieved (euros).

NovaSeq 6000 (2 × 250 bp)DNBSEQ-G400RS* (2 × 200 bp)
Library preparation for sequencing100170
Offer for sequencing 50 million reads1,000170
Actual cost per million raw reads30.077.21
Actual cost per million filtered reads (matrix#1)53.2311.92
Actual cost per raw gigabit (Gb)26.448.25
Actual cost per filtered Gb (merged and quality filtered)104.7633.66

DOI: 10.7717/peerj.12045/table-1

Bioinformatics

NovaSeq and DNBSEQ provided 42,990,088 and 55,581,045 raw reads, respectively. The raw reads were demultiplexed using cutadapt v3.4 (Martin, 2011) by requiring full-length index coverage (–overlap 12) and allowing one mismatch to index sequence (−e 1) but no indels (–no-indels) (Data 1). Fasta-formatted index file specifying sample-index combinations served as an input for cutadapt. The reads were separated into sample-wise files according to this information. During demultiplexing, we also accounted for reverse complementary sequences in the raw data by running two rounds of demultiplexing using cutadapt. For the second round, the unassigned R1 and R2 reads from the first round used as inputs. Demultiplexed reads from both runs were then merged by sample. Prior to further processing, all sequences were reoriented to 5′–3′ orientation based on the PCR primers. For this procedure, fqgrep (v0.4.4; Das, 2011) was used by allowing two mismatches to primer sequences as implemented in PipeCraft v1.0 (Anslan et al., 2017). Paired-end sequences were assembled using vsearch v2.17.0 (Rognes et al., 2016) with the following settings: –fastq_minovlen 10, –fastq_minmergelen 10, –fastq_maxdiffs 20, –fastq_maxns 0, –fastq_maxmergelen 600, –fastq_allowmergestagger. Both forward and reverse primers were trimmed from the sequences using cutadapt by allowing two mismatches to primer strings and primer match overlap of 24 but no indels. Reads where both primers remained undetected were discarded. Quality-filtering of the remaining sequences was performed using trimmomatic v0.39 (Bolger, Lohse & Usadel, 2014) with the following options: SLIDINGWINDOW:5:30, LEADING 11, TRAILING 11. Putative chimeric sequences were removed using the uchime_denovo algorithm as implemented in vsearch (–id 0.97 for pre-clustering, default options for chimera detection). All filtered sequences from both sequencing platforms per sample were merged and clustered into operational taxonomic units (OTUs) with 97% sequence similarity threshold using vsearch (–cluster_size –iddef 2). During the latter process, a uniform sample-by-OTU table (containing data from both platforms) was generated (matrix#1, see below). The resulting OTUs were classified using BLAST+ v2.10.1 (Camacho et al., 2009) against the CO1Classifier database v4 (Porter & Hajibabaei, 2018) (Data 2).

In addition to the OTU workflow, amplicon sequence variants (ASVs) were calculated using DADA2 (v1.18; Callahan et al., 2016). The ASV matrix was generated including a subset of 60 samples from the total of 120 samples that were used for generating OTU matrices (Table S1). The quality filtering options included removal of all sequences with ambiguous bases (maxN = 0), trimming low quality ends (truncQ = 2) and keeping sequences with maximum expected error rates of one (maxEE = 1). Chimeras were removed with the ‘consensus’ method. All other processes, including denoising, followed the default DADA2 workflow (Data 1). Inputs for the ASV pipeline included fastq files for each sample that comprised primer-trimmed and reoriented reads (as specified above). The ASVs were further subjected to post-clustering at 97% sequence similarity threshold using the LULU algorithm (Frøslev et al., 2017) to merge consistently co-occurring ASVs. Thus, two sets of ASVs matrices were generated for the analyses: (1) ASVs matrix, and (2) post-clustered ASVs matrix (Data 3).

OTU data matrices

Four types of sample-by-OTU data matrices were prepared (Data 1) to compare the DNBSEQ and NovaSeq sequencing platforms: (1) matrix#1—all ‘raw’ OTUs as outputted after the clustering step; (2) matrix#2—‘raw’ OTUs but global singletons (i.e., OTUs that had only one sequence across matrix#1) removed; (3) matrix#3—only metazoan global non-singleton OTUs; (4) matrix#4—rarefied metazoan OTUs. To account for variations in sequencing depth, metazoan OTUs data (matrix#3) was rarefied using phyloseq v1.34.0 (McMurdie & Holmes, 2013) to a depth of 10,489 reads per sample (matrix#4). To reduce the remaining putative artefacts in the matrix#3, OTUs with a representative sequence length different from the expected amplicon length (313 bp ± 4 bp) were discarded. This eliminated 59% (82,036) of OTUs accounting for 31.8% (15,639,811) of reads from matrix#2. Besides Metazoa, the used COI primers amplified a wide variety of other non-target eukaryotes (mostly fungi) as well as prokaryotes. An OTU was assigned to Metazoa (in matrix#3) when the best blastn match of the query sequence had at least 90% query coverage and 75% identity against the reference sequence. For metazoan taxonomic group statistics, an OTU received a phylum level classification when the best blastn match of the query sequence (an OTU) had ≥80% identity against the reference sequence with phylum-level annotation. Some OTUs were best matched to Hydrozoa and Porifera at <89% sequence similarity, but since these aquatic organisms are unexpected in terrestrial environments, we assigned these OTUs to unclassified Metazoa.

Statistics

Differences in the OTU/ASV richness between DNBSEQ and NovaSeq data sets were tested using paired t-tests in STATISTICA (v7; StatSoft Inc., Tulsa, OK, USA). For ASV matrices and OTU matrices#1–3, we first calculated the predicted richness values based on residuals of OTU richness, as derived from linear regression analyses using natural logarithm transformed sequencing depth as an independent variable, separately for DNBSEQ and NovaSeq data subsets. For the OTU matrix#4, residuals were not calculated because of using rarefaction. Spearman correlation was used to examine the sequencing depth and OTU richness correlations between sequencing platforms. Mantel tests (with 9999 permutations, method = ‘spear’), as implemented in the ‘vegan’ package v2.5.7 (Oksanen et al., 2015) in R v4.0.4 (R-Core-Team, 2021), were used to test correlations between corresponding sample similarities from different sequencing platforms. Additionally, Procrustes tests (with 9999 permutations, metaMDS ordination), as implemented in the ‘vegan’ package, were used to compare correlation in community structure as revealed from DNBSEQ and NovaSeq instruments. Bray–Curtis similarity of Hellinger-transformed data were used for both Mantel and Procrustes tests. To assess OTU/ASV overlap between sequencing platforms, Venn diagrams were drawn using Venny 2.1 (Oliveros, 2018). The proportion of potential index-switching errors was estimated using the UNCROSS2 score (Edgar, 2018) with default parameter values (f = 0.01, tmin = 0.1) for each sample and sequencing platform combination. Differences among sequencing platforms were tested using a Bayesian generalized linear mixed model with binomial errors and logit link, where ‘sample’ was used as a random effect. The model was fitted with Stan v2.21 (Stan-Development-Team, 2021) and brms package v2.15.0 (Bürkner, 2017) using seven Markov chains of Hamiltonian Monte Carlo, with 15,000 sampling iterations and 2,000 warm-up iterations for each chain.

Results

Demultiplexed HTS datasets of 120 samples from DNBSEQ and NovaSeq contained 50,129,600 and 39,813,707 sequences, respectively. The overall quality score distributions exhibited similar profiles between DNBSEQ and NovaSeq datasets (Fig. 1; Fig. S1). However, the latter exhibited marginally higher level of expected number of errors in the sequences (Fig. 1A). Therefore, after filtering (all filtering steps before clustering in the OTU workflow), proportionally more sequences were discarded in the NovaSeq data (48.1%) compared with DNBSEQ data (43.1%; Table S3). Similarly, after the ASVs workflow (for the subset of 60 samples out of 120), the average proportional sequence loss per sample was higher in NovaSeq data (29.5% vs. 33.4%; Table S3).

Quality profile.

Clustering at 97% sequence similarity threshold (both datasets merged) revealed 182,066 OTUs including 43,136 singletons. Of the 138,930 non-singleton OTUs, 17,547 (12.6%) were unique to DNBSEQ and 20,175 (14.5%) unique to NovaSeq (Fig. 2A). These unique OTUs usually comprised a low number of reads, with a median sequence count of 3 (±13.7 SD) and 5 (±61.1 SD) for DNBSEQ and NovaSeq data, respectively. The proportion of shared OTUs between datasets was 72.8% for all non-singleton OTUs (matrix#2), 96.6% for the full metazoan dataset (matrix#3) and 85.2% for the rarefied metazoan dataset (matrix#4; Fig. 2). Total number of ASVs (in a subset of 60 samples, across both data sets) was 121,402, including 2,660 global singletons. Post-clustering those ASVs with 97% sequence similarity threshold with LULU algorithm merged 17,756 ASVs (14.6%), retaining 103,646 ASVs (including global 343 singletons). The proportion of shared ASVs between post-clustered ASVs data set was 53.5% (Fig. 2D). The unique post-clustered ASVs per platform had median sequence count of 7 (±56.9 SD) and 5 (±42.0 SD) for DNBSEQ and NovaSeq data, respectively.

Venn diagrams.

Taxonomic composition

Metazoa contributed to 13.2% and 12.7% OTU richness, and 18.6% and 24.4% total read abundance, in the DNBSEQ and NovaSeq datasets, respectively. Within Metazoa, the largest phyla in terms of OTU richness were Arthropoda and Nematoda, whereas the largest classes were Insecta (Arthropoda) and Chromadorea (Nematoda) (Fig. 3). While the distribution of relative OTU numbers was highly similar across sequencing platforms, there were certain differences in relative abundance of reads. In the DNBSEQ data, relatively more reads of unclassified Metazoa were recovered at the expense of Annelida (Fig. 3). Taxonomic annotation of ASVs were not performed in this study.

Taxonomy.

Sequencing depth and diversity

While the total sequencing depth among platforms depended on the sequencing amount ordered and cannot be thus compared, the relative proportion of reads per sample was highly similar across the two platforms (min: 0.18% and 0.18%; max: 2.57% and 2.66%; median: 0.73% and 0.73%; average: 0.83% and 0.83% for DNBSEQ and NovaSeq, respectively; Fig. S2). All four OTU matrices exhibited strong correlations in per-sample OTU richness between sequencing platforms (Fig. 4A), with Spearman correlation coefficients 0.974, 0.974, 0.994 and 0.970 for raw OTUs (matrix#1), non-singleton OTUs (matrix#2), metazoan OTUs (matrix#3) and rarefied metazoan OTUs (matrix#4), respectively (P < 0.001 for all tests). Similarly, all OTU matrices exhibited strong correlations in per-sample sequence abundance between sequencing platforms (Fig. 4B), with Spearman correlation coefficients 0.975, 0.975 and 0.912 for matrix#1, matrix#2 and matrix#3, respectively (P < 0.001 for all cases). In addition, community composition retrieved by DNBSEQ and NovaSeq platforms were strongly correlated based on Procrustes (R ≥ 0.97 and P < 0.001 for all tests) and Mantel (mantel R ≥ 0.991 and P < 0.001 for all tests) statistics (Fig. 5). These patterns were the nearly identical when comparing ASV matrices of DNBSEQ and NovaSeq (Fig. 5; Fig. S3).

Richness correlations.
Community correlations.

There was a significant difference in OTU richness between DNBSEQ and NovaSeq data in all OTU matrices (matrix#1: t = 39.191, df = 119, P < 0.001; matrix#2: t = 40.140, df = 119, P < 0.001; matrix#3: t = 15.755, df = 119, P < 0.001; and matrix#4: t = 22.723, df = 119, P < 0.001) (Figs. 6A–6C). For example, an average per-sample OTU richness was 9.7% higher in DNBSEQ data in the rarefied metazoan dataset (matrix#4). The rank abundance curves of OTUs (matrix#4) derived from DNBSEQ and NovaSeq displayed a slightly different pattern (Fig. 7). There was a slight tendency towards greater dominance in the NovaSeq dataset, with the top three abundant OTUs being more abundant by a factor of 2.9, 3.2 and 1.9. We further explored the differences in OTU richness between data sets by removing all potential ‘noise’ of spurious OTUs by further filtering matrix#4 (rarefied metazoan OTU table) to include only OTUs with relative sequence abundance of ≥0.01% (per data set) and ≥98% sequence similarity to the reference sequences (matrix#5 in Data 1). In this stringently filtered data set, differences in OTU richness disappeared (paired t-test: t = 0.131, df = 119, P = 0.896; Fig. 6D). Similarly, there were no significant differences in the ASV richness between DNBSEQ and NovaSeq data sets in the ASV matrices (P > 0.9; Figs. 6E and 6F).

Richness.
Rank abundance curves (RAC).

Index-switching errors

The UNCROSS2 score revealed that index-switching errors were slightly higher in the DNBSEQ OTU matrices #1 and #2 (Fig. 8; Table S5). For example, the overall proportion of reads that represent putative index switches were 0.049% and 0.038% for DNBSEQ and NovaSeq data in matrix#1, respectively (Fig. 8). However, in the rarefied metazoan dataset (matrix#4), the DNBSEQ matrix displayed a lower proportion of index-switching errors compared with the NovaSeq data (0.021% vs. 0.043%; Fig. 8; Table S5). This indicates that rarefaction either lowers the detection of index-switching errors or the majority of index-switches (which occur in low abundances) were removed during the process. However, compared with the OTU matrices, the ASV matrices displayed a relatively lower proportion of reads with putative index-switching errors, and the data from both platforms exhibited similar level of index switches (Fig. 8). Procrustes correlations between index-switch corrected and uncorrected OTU tables were high (0.977–0.999; P < 0.001; Fig. S3), indicating that quantitative community-level analyses are weakly impacted by these low proportions of index-switching errors.

Index switches.

Discussion

Recovering OTU/ASV richness and composition

By using the same amplicon pools of ~313-bp COI marker gene fragment for platform-specific library preparation and sequence data generation on DNBSEQ-G400RS and NovaSeq 6000 platforms, we demonstrate strongly correlating community and richness profiles. The overall similarities between two short-read sequencing platforms corroborate earlier studies on metabarcoding of bacteria (Sun et al., 2021) and genomics of various organisms (Jeon et al., 2021; Kim et al., 2021).

The OTU and ASV community patterns showed strong correlations between the sequencing platforms (Fig. 5; Fig. S3), but the DNBSEQ dataset revealed on average 9.7% higher OTU richness per sample (rarefied metazoan OTUs, matrix#4). This may be related to a lower effective read quality leading to generation of spurious OTUs (Edgar 2017). To test this, we clustered the unique NovaSeq and DNBSEQ metazoan OTUs in matrix#4 at 96% sequence similarity. Altogether 10.2% and 12.9% of the unique NovaSeq and DNBSEQ OTUs (in matrix#4) clustered to other OTUs using this relaxed threshold. This difference suggests that the greater number of closely related OTUs may result from a slightly higher proportion of remaining erroneous reads in the DNBSEQ data. Furthermore, the DNBSEQ data exhibited lower relative abundance (in terms of number of reads) of the most common OTUs compared with the NovaSeq dataset (Fig. 7), which may also result from a higher proportion of sequencing errors. However, if this greater dominance is artefactual, occupation of a large proportion of sequences by the dominants may render rare species undetected and result in a lower overall richness (Elbrecht, Peinert & Leese, 2017). Therefore, less biased sequencing depth towards high abundant taxa may result in overall greater detected richness (Elbrecht, Peinert & Leese, 2017). In this study, we did not include a relevant mock community and therefore, we cannot compare whether the results of one or the other platform are closer to the reality in terms of Metazoan diversity. However, the indications about the higher proportion of remaining erroneous reads in the DNBSEQ data (after quality filtering) was also supported by the disappearance of significant differences in the OTU richness when comparing the stringently filtered matrix#5 (Fig. 6D). Furthermore, the ASV matrices demonstrated highly similar richness profiles between different platforms (Figs. 6E and 6F). The ASV workflow included the DADA2 denoising algorithm (Callahan et al., 2016), which seems to efficiently remove the remaining ‘noise’ and resulting in highly concurrent ASV and post-clustered ASV richness profiles between the sequencing platforms (Figs. 6E and 6F). A majority of the ‘noise’ is ‘hidden’ in the molecular units (OTUs or ASVs) with low read count, especially in the sample-wise singletons. Compared with the full ASV matrix (both DNBSEQ and NovaSeq data), the sample-wise singletons were 65 times more abundant in the OTU matrix#2 (a comparison across 60 samples; Data 1), which contributed to the OTU richness differences between sequencing platforms. The bioinformatics workflow with the additional denoising step lowers the fraction of low-abundance spurious molecular units, which inflate the richness (Reitmeier et al., 2021). Additionally, rare OTUs (i.e., OTUs with low number of reads) are poorly reproducible between sequencing runs (Leray & Knowlton, 2017). Therefore, non-stringent quality-filtering may increase richness heterogeneity for the same samples sequenced in different runs (Reitmeier et al., 2021). Despite the differences in OTU richness in our study, the OTU community level analyses from either platform would yield highly corresponding results (as indicated by the high Procrustes and Mantel correlations, >0.97; Fig. 5); however, an additional denoising and filtering low abundant molecular units may aid towards more accurate richness analyses.

Index switches

Potential index-switching errors in the raw OTU matrices #1 and #2 were slightly higher in the DNBSEQ than NovaSeq data (Fig. 8; Table S5). This may be at least partly related to the library preparation processes (by service providers) prior to sequencing. The NovaSeq library preparation included a PCR-free workflow, whereas the DNBSEQ library was subjected to post-ligation PCR which may have significant effect on index switching (Schnell, Bohmann & Gilbert, 2015; Caroe & Bohmann, 2020). While index switches had a negligible impact on the community analyses, such a slightly higher index switch rate in the DNBSEQ data may partly explain the observed differences in per-sample OTU richness (Fig. 6). Following rarefaction (matrix#4), the proportion of potential index-switching errors decreased considerably. This indicates that many potential index-switching errors were removed by discarding a large proportion of sample-wise rare OTUs (with low read abundance), which are more likely to be technical artefacts. Because of higher sequencing depth in the DNBSEQ data in our study (and higher per-sample singleton OTU proportion in matrix#3), latter data set lost proportionally more reads and probably therefore the proportion of putative index switches declined slightly more in the DNBSEQ data (Fig. 8). Because many low abundant sequences (especially singletons) were removed during the ASV workflow, the index switches in the corresponding matrices displayed markedly lower proportion of putative index-switching errors, with a highly similar proportion of index switches remaining in both datasets (Fig. 8). Although index switches are a known issue in high-throughput sequencing platforms (Carlsen et al., 2012; Caroe & Bohmann, 2020; Loit et al., 2019; Schnell, Bohmann & Gilbert, 2015), we found that it had a minor effect on the community structure in our tested datasets (Fig. S5). Nonetheless, being aware of the presence of such errors and applying appropriate data curation prior to statistical analyses are principal requisites of a scientific study (Caroe & Bohmann, 2020; Esling, Lejzerowicz & Pawlowski, 2015).

Limitations

The main limitation of this study is no replication of sequencing runs with companies providing a similar service. However, the single runs of DNBSEQ-G400RS and NovaSeq 6000 revealed similar results, which is unlikely to occur when one or both of these runs have technical issues or biases in library preparation. By testing the reproducibility of 16S amplicon sequencing results from Illumina MiSeq platform, Wen et al. (2017) demonstrated that the OTU community variations were greater between technical replicates that were subjected to different sequencing runs compared with variations that were derived from technical replicates within the same sequencing run. Relatively higher variations between different sequencing runs are likely arising because of the low reproducibility of rare OTUs (i.e., OTUs with low number of reads; Leray & Knowlton, 2017). Here, we intentionally excluded a mock community because we did not access various axenically grown animals.

Conclusions

We demonstrate that the MGI-Tech DNBSEQ-G400RS and Illumina NovaSeq 6000 instruments are both well suited for DNA metabarcoding of COI amplicon libraries of ~313 bases given the similarities in data quality and reconstruction of animal diversity. However, we caution that amplicon length (beyond 350 bases) and length heterogeneity (some amplicons beyond 350 bases such as in fungal Internal Transcribed Spacer, ITS) may become critical for the 2 × 200 paired-end chemistry of the MGI-Tech DNBSEQ-G400RS instrument. We conclude that the main benefit of DNA nanoball sequencing lies in its lower sequencing costs (Table 1).

Supplemental Information

Used PCR primers for COI amplicon library construction.

Only forward primer was indexed. For sequencing, platform specific adapters were ligated by the sequencing

DOI: 10.7717/peerj.12254/supp-3

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The proportion of reads and OTUs with index switch errors for tested OTU table types from DNBSEQ and NovaSeq sequencing platforms.

’index-switch %’ data appears in Figure 8.

DOI: 10.7717/peerj.12254/supp-6

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Sours: https://peerj.com/articles/12254/
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